{"as_of":"2026-08-15T12:54:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bbc74b602613722e291a6f6ac47b08aeb1373b6ed20e2258ab6d9f92287d17cc","coverage":[{"denominator":73,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":73,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T14:06:27.198355Z","state":"measured"},{"denominator":73,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":73,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/1908.03835/citation-record","integrity":"/paper/1908.03835/integrity","json":"/paper/1908.03835/citation-record.json","paper":"/paper/1908.03835"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:28.127432Z","title":"MaskConnect - Con- nectivity Learning by Gradient Descent.ECCV, cs.CV , 2018","venue":null,"work_id":"d84e3504-f7c7-4cb9-b3c2-6ff5d993a36d","year":2018},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:26.884966Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:345ec1639997f1ad3454caa639d4409591d45284afda790b08ebdd4866f70efc","observation_id":"409c8dba-8178-4612-bcd3-c2945bcabe47","resolution":{"observed_at":"2026-08-14T14:06:28.132042Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1701.04862","last_updated":"2017-01-17T20:46:21Z","snapshot_observed_at":"2026-08-15T11:23:36.583037Z","submitted_at":"2017-01-17T20:46:21Z","title":"Towards Principled Methods for Training Generative Adversarial Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1701.04862","snapshot_observed_at":"2026-08-14T14:06:26.889782Z","title":"Towards principled methods for training generative adversarial networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:26.889782Z"},"links":{"cited_paper":"/paper/1701.04862","citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:868b37d51b1f8281e4b2927f777c6e44b5c1adb49ebe25c64f5f6301166afe86","observation_id":"19fd5f4f-7419-4e27-9a8d-2a6ae78c3308","resolution":{"observed_at":"2026-08-14T14:06:26.889782Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:28.115012Z","title":"Wasserstein generative adversarial networks","venue":null,"work_id":"c935f0e8-71f6-4465-96d9-f31218fc3f81","year":2017},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:26.895248Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:2b93a6d8f4ed9b58c0e03289c090d7fbe6474a1b8a23b6c368ef157209d37589","observation_id":"341a84c5-2ea3-4814-bbb0-28e07e4a767a","resolution":{"observed_at":"2026-08-14T14:06:28.119200Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1611.02167","last_updated":"2017-03-22T20:08:30Z","snapshot_observed_at":"2026-08-14T21:31:35.537148Z","submitted_at":"2016-11-07T16:49:43Z","title":"Designing Neural Network Architectures using Reinforcement Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.02167","snapshot_observed_at":"2026-08-14T14:06:26.899684Z","title":"Designing neural network architectures using rein- forcement learning","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:26.899684Z"},"links":{"cited_paper":"/paper/1611.02167","citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:35102e141d8a4e1ef86b24df95dc7d5067f13adbe80dd59ca04a11e79adab67f","observation_id":"1f8667ba-5c11-4884-af58-747f249e55d3","resolution":{"observed_at":"2026-08-14T14:06:26.899684Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1809.11096","last_updated":"2019-02-25T21:32:06Z","snapshot_observed_at":"2026-07-06T07:04:57.275371Z","submitted_at":"2018-09-28T15:38:49Z","title":"Large Scale GAN Training for High Fidelity Natural Image Synthesis","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1809.11096","snapshot_observed_at":"2026-08-14T14:06:26.904346Z","title":"Large scale gan training for high fidelity natural image synthesis","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:26.904346Z"},"links":{"cited_paper":"/paper/1809.11096","citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:30d1e6c8abe48fe886e874b139fa335b9ab294f72f3c026ad29813347f95525a","observation_id":"5ab24be0-7c8a-48be-950d-da5d5b139809","resolution":{"observed_at":"2026-08-14T14:06:26.904346Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1609.07093","last_updated":"2017-02-06T18:46:50Z","snapshot_observed_at":"2026-08-14T21:38:36.194993Z","submitted_at":"2016-09-22T18:07:56Z","title":"Neural Photo Editing with Introspective Adversarial Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.07093","snapshot_observed_at":"2026-08-14T14:06:26.908772Z","title":"Neural photo editing with introspective adversarial networks","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:26.908772Z"},"links":{"cited_paper":"/paper/1609.07093","citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:71d8f21c4cf524ba48936d0e6a387734d5d9a10358c535562314bd7a5ddad7ed","observation_id":"05207827-0929-4371-80fa-e08c3f0c660c","resolution":{"observed_at":"2026-08-14T14:06:26.908772Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:28.102083Z","title":"Searching for efficient multi-scale archi- tectures for dense image prediction","venue":null,"work_id":"77cee45a-0289-438c-89ba-06830dbb6175","year":2018},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:26.914013Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:091a5e3a1453acd6f9083869b8c0315c4481b30b0ea81db11faaad9df24c08d5","observation_id":"350fcaa0-11df-4598-ada3-e7cfa9a47b02","resolution":{"observed_at":"2026-08-14T14:06:28.106538Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:26.918146Z","title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolu- tion, and fully connected crfs","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:26.918146Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:a74b930f4f40e34be6f4e3f39c93247807e3790d506c5bd6ed4e66b4bcfa3ce1","observation_id":"a51b4b5f-ef33-46cd-b18b-bb212c13b9fb","resolution":{"observed_at":"2026-08-14T14:06:26.918146Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1706.05587","last_updated":"2017-12-05T18:06:21Z","snapshot_observed_at":"2026-08-07T13:44:53.690521Z","submitted_at":"2017-06-17T22:48:57Z","title":"Rethinking Atrous Convolution for Semantic Image Segmentation","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.05587","snapshot_observed_at":"2026-08-14T14:06:26.922435Z","title":"Rethinking atrous convolution for seman- tic image segmentation","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:26.922435Z"},"links":{"cited_paper":"/paper/1706.05587","citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:9620563630de03e9844d78276b69b38576e27a01c2d150bfe4f183acee9d8fb1","observation_id":"578c79e0-3c8c-4d4a-884c-73ce757c51bc","resolution":{"observed_at":"2026-08-14T14:06:26.922435Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1707.08819","last_updated":"2017-08-23T16:06:20Z","snapshot_observed_at":"2026-08-14T20:44:55.373539Z","submitted_at":"2017-07-27T11:22:22Z","title":"A Downsampled Variant of ImageNet as an Alternative to the CIFAR datasets","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.08819","snapshot_observed_at":"2026-08-14T14:06:26.927227Z","title":"A downsampled variant of imagenet as an alternative to the ci- far datasets","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:26.927227Z"},"links":{"cited_paper":"/paper/1707.08819","citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:e212edb71c082f0db7768b22c385148f7abb757b89c4dc7abaaf8bea0837128c","observation_id":"38e8e60d-e5f3-4973-84e6-70eb01b8aea8","resolution":{"observed_at":"2026-08-14T14:06:26.927227Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:26.931950Z","title":"Generative adversarial nets","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:26.931950Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:190e8ccf588cd9fad31c67c345d9fb4e14af3c422ed73395538ff4ffb8398218","observation_id":"427944ba-c2db-4d94-b094-f92d81ed4c2f","resolution":{"observed_at":"2026-08-14T14:06:26.931950Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1709.07359","last_updated":"2018-05-17T14:07:35Z","snapshot_observed_at":"2026-08-14T20:30:59.193988Z","submitted_at":"2017-09-21T14:55:54Z","title":"Class-Splitting Generative Adversarial Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1709.07359","snapshot_observed_at":"2026-08-14T14:06:26.936095Z","title":"Class-splitting generative adversarial networks","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:26.936095Z"},"links":{"cited_paper":"/paper/1709.07359","citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:08655e349de0523f35b4f357384eb818c677f348d1b495aac7aff773369ec01e","observation_id":"09570852-dcf0-490c-b66e-2364d3b5bed6","resolution":{"observed_at":"2026-08-14T14:06:26.936095Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:28.071402Z","title":"Improved training of wasserstein gans","venue":null,"work_id":"484b755a-d88a-4598-982d-a90898365234","year":2017},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:26.940080Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:1be1bc1f3d16924ce7e6b31e2b754ad754b6a421fbea343f1cde5b02a33b8233","observation_id":"e13b9d91-7c14-4fb2-9d30-fa19fbb71f96","resolution":{"observed_at":"2026-08-14T14:06:28.076569Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:28.058775Z","title":"Prob- gan: Towards probabilistic gan with theoretical guarantees","venue":null,"work_id":"db86f20c-c525-485d-9719-2e837327ab6d","year":2019},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:26.943789Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:b5e059e048fd701f35d126fef608f9e2fedff8e8a7906b80e979280d503964c7","observation_id":"ca5fa5f7-f535-4e94-af4d-ee03ced7e5da","resolution":{"observed_at":"2026-08-14T14:06:28.062945Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:26.947646Z","title":"Deep residual learning for image recognition","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:26.947646Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:a83ea2f0be1061f124664c6258271900949c9f0c6ac32bc10f4329dec0aad8d3","observation_id":"08ff0c2c-7c34-4933-938f-7b17a360f5ca","resolution":{"observed_at":"2026-08-14T14:06:26.947646Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:26.952769Z","title":"Identity mappings in deep residual networks","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:26.952769Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:54e67056e14c95be3fbbc945fb97de7e6f24831e972ae93d3a7bcf892a199872","observation_id":"f5c74d9b-4dad-4c8a-90f2-70c60eb2db60","resolution":{"observed_at":"2026-08-14T14:06:26.952769Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:28.030307Z","title":"Amc: Automl for model compression and ac- celeration on mobile devices","venue":null,"work_id":"6dec43b9-dcdc-4917-afb8-d1aef0296c80","year":2018},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:26.956992Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:9014b4d74c33e9a7dbdf97989bdde4828647fc03e37252dee82158f464f963a0","observation_id":"05055fce-3d8b-43bf-aef0-7ecbe8f3999a","resolution":{"observed_at":"2026-08-14T14:06:28.034373Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1706.08500","last_updated":"2018-01-12T14:05:44Z","snapshot_observed_at":"2026-08-14T20:51:33.836666Z","submitted_at":"2017-06-26T17:45:23Z","title":"GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1706.08500","snapshot_observed_at":"2026-08-14T14:06:26.961008Z","title":"Gans trained by a two time-scale update rule converge to a nash equilibrium","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:26.961008Z"},"links":{"cited_paper":"/paper/1706.08500","citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:e82e2a6faff39bf1f39c0622a439216c93d1c988fd61362d897dbd953d40ad40","observation_id":"a5752564-d48f-4484-84ea-6957089629ec","resolution":{"observed_at":"2026-08-14T14:06:26.961008Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:28.017979Z","title":"Mgan: Training generative adversarial nets with multiple generators","venue":null,"work_id":"8b05cd61-d697-47fe-9776-f19835b597d1","year":2018},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:26.967038Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:a1a87e84c9de12a01740859df60bfc082c0fe6474eba43a76de8012f9bda2cb7","observation_id":"5cd13a4d-05c7-470b-8dee-9df613e20257","resolution":{"observed_at":"2026-08-14T14:06:28.022254Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:26.972291Z","title":"Long short-term memory","venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:26.972291Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:a5bb88f968123f29e2c0f4626eb1d711725623aafea882465594ea6122480dc1","observation_id":"c6538741-a201-482f-8350-abe0e62e3d06","resolution":{"observed_at":"2026-08-14T14:06:26.972291Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1502.03167","last_updated":"2015-03-02T20:44:12Z","snapshot_observed_at":"2026-08-12T21:01:12.961874Z","submitted_at":"2015-02-11T01:44:18Z","title":"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1502.03167","snapshot_observed_at":"2026-08-14T14:06:26.976773Z","title":"Batch normalization: Accelerating deep network training by reducing internal co- variate shift","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:26.976773Z"},"links":{"cited_paper":"/paper/1502.03167","citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:f9c14535cfe5e148fb9ceb8c07c6871016719c59f2c908584e5a70792189ad0d","observation_id":"9c8ba897-74ad-4ec4-b126-41ec91f02add","resolution":{"observed_at":"2026-08-14T14:06:26.976773Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:27.995414Z","title":"Image-to-image translation with conditional adversar- ial networks","venue":null,"work_id":"b86e7efd-eda5-459c-a554-682a9fcefd4b","year":2017},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:26.980901Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:44e8a205a9d0c23b3ae551d791a2e8c9546c5a43344cf29e2ddf807935c34041","observation_id":"edba19f2-6a5a-47e1-82b9-152bb07c0889","resolution":{"observed_at":"2026-08-14T14:06:28.000153Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.06972","last_updated":"2021-01-24T18:22:22Z","snapshot_observed_at":"2026-08-14T16:14:37.735284Z","submitted_at":"2019-06-17T11:54:20Z","title":"EnlightenGAN: Deep Light Enhancement without Paired Supervision","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.06972","snapshot_observed_at":"2026-08-14T14:06:26.984950Z","title":"Enlightengan: Deep light enhancement without paired supervision","venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:26.984950Z"},"links":{"cited_paper":"/paper/1906.06972","citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:723d425a61f4f49430ff7c097dfdfbcff63b2a88cbf7e21df87eba20d5915334","observation_id":"af33799d-c228-4bc1-bcf6-22e1d4661b7f","resolution":{"observed_at":"2026-08-14T14:06:26.984950Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:27.981020Z","title":"Auto-keras: Ef- ficient neural architecture search with network morphism, 2018","venue":null,"work_id":"13f3c5d0-b7c7-4206-9018-bd9b21b9b102","year":2018},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:26.989748Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:fbb53a72ef3940d81b22a77f708063f841a71d136b6328afd3a61640ca78ed27","observation_id":"ef6a2a3c-ec2c-42a2-aaae-19bfa42c2218","resolution":{"observed_at":"2026-08-14T14:06:27.985601Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.00734","last_updated":"2018-09-10T17:11:59Z","snapshot_observed_at":"2026-08-14T18:56:38.104749Z","submitted_at":"2018-07-02T15:11:23Z","title":"The relativistic discriminator: a key element missing from standard GAN","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.00734","snapshot_observed_at":"2026-08-14T14:06:26.993892Z","title":"The relativistic discriminator: a key element missing from standard gan","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:26.993892Z"},"links":{"cited_paper":"/paper/1807.00734","citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:93d054775a6f145fab4649d1b398657e86908badeafe7cc5ac3c34ae11f008c2","observation_id":"2bd4c822-b041-4ad9-9d5a-f55c0c427d05","resolution":{"observed_at":"2026-08-14T14:06:26.993892Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:27.966724Z","title":"Progressive growing of gans for improved quality, stability, and variation","venue":null,"work_id":"0c0d5555-57bd-4720-9f31-95edef1002ac","year":2018},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:26.998500Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:6e5c8572d27641ff06c0b30f6ce98ce2283e78b149f07f92e26fa0a59bbc6374","observation_id":"8e8b724f-37e5-4357-a423-c6809d45ed1a","resolution":{"observed_at":"2026-08-14T14:06:27.971257Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1812.04948","last_updated":"2019-03-29T11:08:46Z","snapshot_observed_at":"2026-08-14T17:44:45.577553Z","submitted_at":"2018-12-12T13:59:43Z","title":"A Style-Based Generator Architecture for Generative Adversarial Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1812.04948","snapshot_observed_at":"2026-08-14T14:06:27.002788Z","title":"A style-based generator architecture for generative adversarial networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.002788Z"},"links":{"cited_paper":"/paper/1812.04948","citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:b0527e1717bcb13d1c9143933d2109a2fa266ee3b0a20de5110aa169ab1626a4","observation_id":"f3f361c1-9437-401d-bc2a-64e228164d3c","resolution":{"observed_at":"2026-08-14T14:06:27.002788Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-14T18:51:16.666127Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-14T14:06:27.007189Z","title":"Adam: A method for stochastic optimization","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.007189Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:65863ea686db53fe177ad36650f49222d0499e71c46ccd2b7bbff92048a42d04","observation_id":"9113298c-0405-43dc-9f21-d3eff8d67912","resolution":{"observed_at":"2026-08-14T14:06:27.007189Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:27.011426Z","title":"Learning multiple layers of features from tiny images","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.011426Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:33d0284c7d58f7f527bc91f2d27a3a9c0228449812aec00fb04441cb2736a522","observation_id":"49af8288-4086-41c6-8298-df5aa431b647","resolution":{"observed_at":"2026-08-14T14:06:27.011426Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:27.015358Z","title":"Imagenet classification with deep convolutional neural net- works","venue":null,"work_id":null,"year":2012},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.015358Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:9648ee4694ec0258b34d68572946ad3d9cf76098761c46c8673c582701830ad7","observation_id":"01ddf822-1d3f-44fb-829e-66d0e60936f1","resolution":{"observed_at":"2026-08-14T14:06:27.015358Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:27.019004Z","title":"Deblurgan: Blind motion deblurring using conditional adversarial networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.019004Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:c1045f30eb88f4e59cc739fb764234e3c73126b94f94012cdf010b8eb2bfe8c7","observation_id":"77d48a16-b2f7-4663-b69e-bc3febeac76a","resolution":{"observed_at":"2026-08-14T14:06:27.019004Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1807.04720","last_updated":"2019-05-14T13:41:42Z","snapshot_observed_at":"2026-08-14T18:52:39.404750Z","submitted_at":"2018-07-12T16:56:50Z","title":"A Large-Scale Study on Regularization and Normalization in GANs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.04720","snapshot_observed_at":"2026-08-14T14:06:27.022681Z","title":"The gan landscape: Losses, archi- tectures, regularization, and normalization","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.022681Z"},"links":{"cited_paper":"/paper/1807.04720","citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:6347f0eb3fbf4b4a7bf7666ea9c2ec9c7d423ac8fece465da33131085e337d7c","observation_id":"3102d0ac-9dd0-4b59-8f6e-c99f24025417","resolution":{"observed_at":"2026-08-14T14:06:27.022681Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:27.922739Z","title":"Photo- realistic single image super-resolution using a generative ad- versarial network","venue":null,"work_id":"703c7614-4337-49fd-891f-825ddd22615d","year":2017},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.026850Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:63ddc9b951a67cf3f17c8883ae30c8e3bc5a5d1ac6c88378cd09ab72a08140b4","observation_id":"48003381-5217-4744-8128-c74f70a88b28","resolution":{"observed_at":"2026-08-14T14:06:27.928742Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:27.907637Z","title":"Random search and repro- ducibility for neural architecture search","venue":null,"work_id":"884755bc-7454-406c-ab9d-38f8387a4e71","year":2019},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.030934Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:0c6a76733f76aea4e9235d38c7068fd532f38385a8bc02b1a98660f11285fe04","observation_id":"84ef1d3b-6074-405d-81c4-df404635b805","resolution":{"observed_at":"2026-08-14T14:06:27.912603Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:27.894438Z","title":"Feature pyramid networks for object detection","venue":null,"work_id":"43e7f947-02d4-4404-8649-466e65131379","year":2017},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.036197Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:8541bd20841871cf77c92d8f7107100483d6671b2a37d08cc15adf43838d02f0","observation_id":"5f34c118-1e39-4544-997b-ef35ee8a0d6a","resolution":{"observed_at":"2026-08-14T14:06:27.898870Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1901.02985","last_updated":"2019-04-06T19:40:44Z","snapshot_observed_at":"2026-08-14T17:32:56.867577Z","submitted_at":"2019-01-10T01:05:15Z","title":"Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1901.02985","snapshot_observed_at":"2026-08-14T14:06:27.040167Z","title":"Auto-deeplab: Hierarchical neural architecture search for semantic image segmentation","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.040167Z"},"links":{"cited_paper":"/paper/1901.02985","citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:a729a80fbfc64b899aa51d9423e2a89bb13144d2f0a1c471fed424c8727e6360","observation_id":"807103f2-8c86-431d-add8-e4f1c7ccdccb","resolution":{"observed_at":"2026-08-14T14:06:27.040167Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:27.881349Z","title":"Progressive Neural Architecture Search","venue":null,"work_id":"272bce56-acb5-45f8-b6be-4a2785073b95","year":2018},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.044409Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:889c29f7575e3f6d7b826a142488803173523d41df1ec04e15d2f96418310c98","observation_id":"9e30b27f-e15f-45c8-9504-b66bf1ddd4ca","resolution":{"observed_at":"2026-08-14T14:06:27.885800Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.09055","last_updated":"2019-04-23T06:29:32Z","snapshot_observed_at":"2026-08-15T06:40:14.261008Z","submitted_at":"2018-06-24T00:06:13Z","title":"DARTS: Differentiable Architecture Search","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1806.09055","snapshot_observed_at":"2026-08-14T14:06:27.048337Z","title":"Darts: Differentiable architecture search","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.048337Z"},"links":{"cited_paper":"/paper/1806.09055","citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:51ff643b91e80ded8234a514b6fec5204c04cf7deba1668d90acd6e35e8f5aab","observation_id":"0a7e643c-8496-4a7d-890e-0fee87a51221","resolution":{"observed_at":"2026-08-14T14:06:27.048337Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:27.864266Z","title":"Are gans created equal? a large-scale study","venue":null,"work_id":"9028a033-4bfd-493b-b917-c321c6ec171f","year":2018},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.052740Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:16cfa51dace2d1c0d660474c250768ddd21be63c03b34e3d6043dc268387ee49","observation_id":"3b3503f1-761c-4825-a780-107337aec3f5","resolution":{"observed_at":"2026-08-14T14:06:27.869236Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.06114","last_updated":"2016-03-01T10:55:58Z","snapshot_observed_at":"2026-08-14T22:22:15.501004Z","submitted_at":"2015-11-19T10:24:14Z","title":"Multi-task Sequence to Sequence Learning","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.06114","snapshot_observed_at":"2026-08-14T14:06:27.057100Z","title":"Multi-task sequence to sequence learning","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.057100Z"},"links":{"cited_paper":"/paper/1511.06114","citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:25328a2c701773cdba5458a7048a11cab12f5c1a42a52af72c2db7545c728c6f","observation_id":"edc99275-c45a-4508-bd09-5505c2ab188a","resolution":{"observed_at":"2026-08-14T14:06:27.057100Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1411.1784","last_updated":"2014-11-06T22:33:22Z","snapshot_observed_at":"2026-08-12T18:51:00.108451Z","submitted_at":"2014-11-06T22:33:22Z","title":"Conditional Generative Adversarial Nets","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1411.1784","snapshot_observed_at":"2026-08-14T14:06:27.061351Z","title":"Conditional generative adversarial nets","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.061351Z"},"links":{"cited_paper":"/paper/1411.1784","citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:0020ad63b99b678e4cb09591e7962179fc8d1489c6b601b8b40037a6522b3e2f","observation_id":"a581a981-b8c4-4c26-b319-4d1b3e9d4fe3","resolution":{"observed_at":"2026-08-14T14:06:27.061351Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.05957","last_updated":"2018-02-16T14:41:39Z","snapshot_observed_at":"2026-08-14T19:45:13.064940Z","submitted_at":"2018-02-16T14:41:39Z","title":"Spectral Normalization for Generative Adversarial Networks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.05957","snapshot_observed_at":"2026-08-14T14:06:27.066216Z","title":"Spectral normalization for generative ad- versarial networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.066216Z"},"links":{"cited_paper":"/paper/1802.05957","citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:2841803aeb8690f4394112b52c3baa576c84341d273f4427aa67ef1bd9ee6227","observation_id":"9d346c09-648e-408e-b4a5-4ecba22a7ca1","resolution":{"observed_at":"2026-08-14T14:06:27.066216Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:27.848187Z","title":"Stacked hour- glass networks for human pose estimation","venue":null,"work_id":"f52e1d76-bf60-4927-9f5e-61e0a0de058b","year":2016},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.070758Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:7b228cf7ef4d899cb66973a27a132784ba5b4902a28bdfeea7e80dd1b6070f56","observation_id":"5d400c10-82fa-4190-a752-4b19e8a2ee3c","resolution":{"observed_at":"2026-08-14T14:06:27.853818Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:27.833628Z","title":"Dual dis- criminator generative adversarial nets","venue":null,"work_id":"21203956-2ce2-40f6-a6f7-398d238c44b6","year":2017},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.075705Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:864822ea5ca89e8efcf950b82f83d86c48100ea1babac5fcf452777543e847a0","observation_id":"3b6691e0-b339-4523-92cf-209fcc79d2ab","resolution":{"observed_at":"2026-08-14T14:06:27.838462Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:27.819493Z","title":"De- convolution and checkerboard artifacts","venue":null,"work_id":"15c9cc70-1b92-42df-9411-cd79df812ee6","year":2016},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.079610Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:1967f864721836af21d747f2ef724d1e4c3e3d4cd4a70c681585b2941deee55d","observation_id":"1e0d1a4a-a377-41d4-b9df-74f217600d35","resolution":{"observed_at":"2026-08-14T14:06:27.823938Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:27.804869Z","title":"Efficient Neural Architecture Search via Parameter Sharing","venue":null,"work_id":"50469430-1f5e-4b92-a930-295703d7569e","year":2018},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.083474Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:477448b9c6fdf7d7e633a11c06bcfb2f541df51e5ec9db9de050eeb276b18de9","observation_id":"b179f264-6851-408e-8261-88cf89545885","resolution":{"observed_at":"2026-08-14T14:06:27.809263Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1511.06434","last_updated":"2016-01-07T23:09:39Z","snapshot_observed_at":"2026-08-12T18:43:14.431633Z","submitted_at":"2015-11-19T22:50:32Z","title":"Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.06434","snapshot_observed_at":"2026-08-14T14:06:27.087153Z","title":"Un- supervised representation learning with deep convolu- tional generative adversarial networks","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.087153Z"},"links":{"cited_paper":"/paper/1511.06434","citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:ad94c41d8e6b2d2e243fee68444d38f20823f9b3147bcfe3cc1eccad95202a9c","observation_id":"6f0665ee-0f58-43bf-ac4e-a6a1af214ac7","resolution":{"observed_at":"2026-08-14T14:06:27.087153Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:27.789983Z","title":"Generative adversarial text to image synthesis","venue":null,"work_id":"bc9796cc-773c-4ab6-9c57-f5e2cea9c841","year":2016},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.091465Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:8502e063dd9049403140491b74da2d63859fcff86beb8af120fe695ffed00788","observation_id":"b832c7d5-69b2-427c-b869-e05f1d822436","resolution":{"observed_at":"2026-08-14T14:06:27.795340Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:27.095505Z","title":"U- net: Convolutional networks for biomedical image segmen- tation","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.095505Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:f2997d0bc51a64d68d11a84d0da34f5fe952b6cc42766679d8fca809f210823b","observation_id":"06a3465d-0def-4273-84f9-2c2adfb8cfb6","resolution":{"observed_at":"2026-08-14T14:06:27.095505Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:27.768559Z","title":"Improved techniques for training gans","venue":null,"work_id":"50f04db1-84fc-4eb0-bc98-a3c31bd3e5af","year":2016},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.099408Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:d02e8d452af0e9205c2ecd3984024994f98de4bfed45f6b19a51d341ac45ab87","observation_id":"3f1a8747-0565-466d-8974-8cfde932718e","resolution":{"observed_at":"2026-08-14T14:06:27.772615Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:27.103876Z","title":"Going deeper with convolutions","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.103876Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:ccc86b1587d58e684045ac0304b2e37234630c32bc4d66bc22f4902de507835c","observation_id":"7dba6048-385a-411e-a1ae-8831d1c58895","resolution":{"observed_at":"2026-08-14T14:06:27.103876Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:27.108481Z","title":"Rethinking the inception archi- tecture for computer vision","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.108481Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:f8271aef75aa6abaf6562d2c9c9a25d569d697db23e2c57325f8c5c158efc537","observation_id":"dbf5c3a8-95ec-48aa-b061-217be3d31dbf","resolution":{"observed_at":"2026-08-14T14:06:27.108481Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1702.08896","last_updated":"2017-11-05T01:52:45Z","snapshot_observed_at":"2026-08-14T21:14:18.903574Z","submitted_at":"2017-02-28T18:33:32Z","title":"Hierarchical Implicit Models and Likelihood-Free Variational Inference","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1702.08896","snapshot_observed_at":"2026-08-14T14:06:27.112565Z","title":"Deep and hierarchical implicit models","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.112565Z"},"links":{"cited_paper":"/paper/1702.08896","citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:c671730b310439e684143039f4125e74d5809f65d47a51b1b4f2db96c25a1ac0","observation_id":"4d55723b-7a63-443e-ac55-c32c57fa8d46","resolution":{"observed_at":"2026-08-14T14:06:27.112565Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:27.739015Z","title":"Dist-gan: An improved gan using distance constraints","venue":null,"work_id":"b4872151-6938-4d3e-83f1-b356884f73a6","year":2018},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.116879Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:4a6d912ff9ef27df0fddad7d51106fd56fd1cbf3cd166c0e5f95c907e82fd2b9","observation_id":"402fd821-9054-4066-be71-a07e3bd50edd","resolution":{"observed_at":"2026-08-14T14:06:27.743139Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1607.08022","last_updated":"2017-11-06T14:21:43Z","snapshot_observed_at":"2026-08-14T21:46:37.530891Z","submitted_at":"2016-07-27T10:23:00Z","title":"Instance Normalization: The Missing Ingredient for Fast Stylization","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1607.08022","snapshot_observed_at":"2026-08-14T14:06:27.120622Z","title":"In- stance normalization: The missing ingredient for fast styliza- tion","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.120622Z"},"links":{"cited_paper":"/paper/1607.08022","citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:9f391d8e6e0c54342f7d1ba2e78e14495fc9d95fd753170c1a9abb1b23776d6a","observation_id":"d258e9cd-b97f-4220-8040-5a065de2cc08","resolution":{"observed_at":"2026-08-14T14:06:27.120622Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.09916","last_updated":"2019-02-08T06:28:35Z","snapshot_observed_at":"2026-08-14T17:38:53.109513Z","submitted_at":"2018-12-24T13:23:18Z","title":"Improving MMD-GAN Training with Repulsive Loss Function","version":4},"cited_work":{"arxiv_id":"1812.09916","doi":null,"metadata_source":"pith","pith_arxiv_id":"1812.09916","snapshot_observed_at":"2026-08-14T14:06:27.328730Z","title":"Improving MMD-GAN Training with Repulsive Loss Function","venue":"cs.LG","work_id":"3826d1b2-347d-4d7e-8ffd-bf7588ddef3c","year":2018},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.124893Z"},"links":{"cited_paper":"/paper/1812.09916","citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:95e33a4fcf4636a20bc3a808038a12d8ff7e8d77d8d28836db4f627a4be75bc0","observation_id":"528e5bf2-29c1-4097-89fb-1a773e963bae","resolution":{"observed_at":"2026-08-14T14:06:27.333291Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:27.725726Z","title":"Studying very low resolution recogni- tion using deep networks","venue":null,"work_id":"fe38f57a-ec43-490b-ac66-9cd3da203955","year":2016},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.128982Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:50a168db8c370b0f2380bc9897a6a21bba076469903b4f4d8c5e27cbb02fcd75","observation_id":"2469af85-605a-4947-ab81-b61dcffbb926","resolution":{"observed_at":"2026-08-14T14:06:27.730867Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:27.712183Z","title":"Improving gen- erative adversarial networks with denoising feature match- ing.(2017)","venue":null,"work_id":"49af691f-32a8-4d63-86a0-7bd61390ff79","year":2017},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.133649Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:743b44c7dab9168f2fdddf5f928a158886c574f659ec81902da3fe27f7918e73","observation_id":"ae178d96-2cca-41f4-bd55-2566b275c6c9","resolution":{"observed_at":"2026-08-14T14:06:27.716357Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:27.699393Z","title":"Simple statistical gradient-following al- gorithms for connectionist reinforcement learning","venue":null,"work_id":"ec24ab67-c489-40cc-9416-dfda4becee52","year":1992},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.137832Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:45baf16426ac260707f9f31df6c29fcb76448b7c18c4e7d3979264fb9385c710","observation_id":"e6cdd04b-c25e-4ef5-9cec-6cfdab4cf611","resolution":{"observed_at":"2026-08-14T14:06:27.703967Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:27.687346Z","title":"Genetic cnn","venue":null,"work_id":"3da0cf08-4092-49bd-954f-0229e0cc0e31","year":2017},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.141732Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:d94a1aa021ebbd394053a6785d6997f1dbc536be28e2ffabb21d75695c6376b4","observation_id":"b9205925-ddbf-4b7a-99a6-a0a8cec10b6b","resolution":{"observed_at":"2026-08-14T14:06:27.691484Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:27.674221Z","title":"Attngan: Fine- grained text to image generation with attentional generative adversarial networks","venue":null,"work_id":"6391a621-8d8c-4659-bd75-b4fc9f24faa7","year":2017},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.145681Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:652836f838ee1aa1c41b361480f2644c91154e0b561bb934f6933ba08c36004f","observation_id":"09d5bbe1-5155-410c-80e4-9e45c3103216","resolution":{"observed_at":"2026-08-14T14:06:27.678948Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1703.01560","last_updated":"2017-08-02T03:51:57Z","snapshot_observed_at":"2026-08-14T21:13:31.220477Z","submitted_at":"2017-03-05T05:17:56Z","title":"LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation","version":3},"cited_work":{"arxiv_id":"1703.01560","doi":null,"metadata_source":"pith","pith_arxiv_id":"1703.01560","snapshot_observed_at":"2026-08-14T14:06:27.312508Z","title":"LR-GAN: Layered Recursive Generative Adversarial Networks for Image Generation","venue":"cs.CV","work_id":"3c0c70d9-7921-4aa6-9cbc-338a6f933f21","year":2017},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.149501Z"},"links":{"cited_paper":"/paper/1703.01560","citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:095ccb6e4f8544af226bdc417f946a4d69096d6b9c65c86fac32d0466cb36765","observation_id":"e9359d41-164f-4590-b647-745076493321","resolution":{"observed_at":"2026-08-14T14:06:27.316848Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.01354","last_updated":"2019-08-10T16:10:08Z","snapshot_observed_at":"2026-08-14T16:37:31.928872Z","submitted_at":"2019-05-03T20:05:18Z","title":"Controllable Artistic Text Style Transfer via Shape-Matching GAN","version":2},"cited_work":{"arxiv_id":"1905.01354","doi":null,"metadata_source":"pith","pith_arxiv_id":"1905.01354","snapshot_observed_at":"2026-08-14T14:06:27.292653Z","title":"Controllable Artistic Text Style Transfer via Shape-Matching GAN","venue":"cs.CV","work_id":"6a324fc8-622d-4835-ae14-6b257ed4ca3d","year":2019},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.154102Z"},"links":{"cited_paper":"/paper/1905.01354","citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:ca0f2fd206d8d1549ba743b2d061e8b666062e3072522bbdcf081fa16be55ad9","observation_id":"521c9c68-54be-42b4-9e46-9e7f816b2226","resolution":{"observed_at":"2026-08-14T14:06:27.299515Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.08318","last_updated":"2019-06-14T18:20:10Z","snapshot_observed_at":"2026-08-15T09:55:20.680511Z","submitted_at":"2018-05-21T23:10:35Z","title":"Self-Attention Generative Adversarial Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.08318","snapshot_observed_at":"2026-08-14T14:06:27.158973Z","title":"Self-attention generative adversarial networks","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.158973Z"},"links":{"cited_paper":"/paper/1805.08318","citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:68d6033c937db7cb20ee7b036033159fa36df6e1668cb7287392f17ac463ee1f","observation_id":"411a4dfa-69b2-4719-8549-ddd6cb7d99e4","resolution":{"observed_at":"2026-08-14T14:06:27.158973Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1710.10916","last_updated":"2018-06-28T00:49:19Z","snapshot_observed_at":"2026-08-14T20:22:05.208461Z","submitted_at":"2017-10-19T18:45:59Z","title":"StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1710.10916","snapshot_observed_at":"2026-08-14T14:06:27.163028Z","title":"Stack- gan++: Realistic image synthesis with stacked generative ad- versarial networks","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.163028Z"},"links":{"cited_paper":"/paper/1710.10916","citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:2086e9d1f8cd69267cdedf4cf7c0d1121f60cbfe9c214c14f8e332d0e3c4a2b8","observation_id":"94362dbd-3c60-4ebf-8a67-c3ad143162bd","resolution":{"observed_at":"2026-08-14T14:06:27.163028Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:27.661805Z","title":"Stack- gan: Text to photo-realistic image synthesis with stacked generative adversarial networks","venue":null,"work_id":"704f7890-c77b-452a-bf09-e1275242a6e4","year":2017},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.167991Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:2c23224e2df71001eb8e83931ed0ffcd640121301db906e01a2e33250dcb09f3","observation_id":"1395a7f7-01bb-4ae5-9923-ba262002c457","resolution":{"observed_at":"2026-08-14T14:06:27.665905Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:27.649043Z","title":"Dada: Deep adversarial data augmentation for ex- tremely low data regime classification","venue":null,"work_id":"07a08244-80b6-4359-be97-6bf9ec63cda2","year":2019},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.172314Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:0c7ffbf8d2cb739c59f713d5249ff33d8a1f28cbacf682db21f9be6f2fc3d8b6","observation_id":"ba75e642-8f85-468e-aeed-429a9be910f8","resolution":{"observed_at":"2026-08-14T14:06:27.653257Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1609.03126","last_updated":"2017-03-06T22:52:53Z","snapshot_observed_at":"2026-08-14T21:40:26.082374Z","submitted_at":"2016-09-11T07:11:13Z","title":"Energy-based Generative Adversarial Network","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1609.03126","snapshot_observed_at":"2026-08-14T14:06:27.176408Z","title":"Energy- based generative adversarial network","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.176408Z"},"links":{"cited_paper":"/paper/1609.03126","citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:614393cc8cf333219cbf0f9f8ae4ac2df4bb94a9e2d1450cec441cc6d90a86bd","observation_id":"01b6580a-5f73-48e8-9bcd-202dbdad7c65","resolution":{"observed_at":"2026-08-14T14:06:27.176408Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:27.636764Z","title":"Practical block-wise neural network architecture gener- ation","venue":null,"work_id":"24723930-e53a-4755-95ce-17aa434106e4","year":2018},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.180416Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:836aa196baff2321b6afdfbf3f33456d9d16c985cba736bf2840ff5eb8f0e1b6","observation_id":"80880f5f-1972-4009-8e0e-e2b4df0a4ce1","resolution":{"observed_at":"2026-08-14T14:06:27.640872Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:27.623994Z","title":"Unpaired Image-to-Image Translation using Cycle- Consistent Adversarial Networks","venue":null,"work_id":"b69a141a-194e-436d-834b-c66cba593033","year":2017},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.184547Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:87456d89bcedeb547eaa18c858f1ed877986261cf8dcf9ec69168859959bbf27","observation_id":"00f80a6c-cb7f-4561-a155-f36e8ca7ab1c","resolution":{"observed_at":"2026-08-14T14:06:27.628331Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1611.01578","last_updated":"2017-02-15T05:28:05Z","snapshot_observed_at":"2026-08-14T21:31:52.294004Z","submitted_at":"2016-11-05T00:41:37Z","title":"Neural Architecture Search with Reinforcement Learning","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.01578","snapshot_observed_at":"2026-08-14T14:06:27.189297Z","title":"Neural architecture search with reinforcement learning","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.189297Z"},"links":{"cited_paper":"/paper/1611.01578","citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:2abdeed52ce4e25052bea2ce7ca8581ae66454a5d285058a55a674a460d6ad22","observation_id":"f3ff6c21-1979-482e-baa8-30b0391f6726","resolution":{"observed_at":"2026-08-14T14:06:27.189297Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:06:27.611519Z","title":"Learning Transferable Architectures for Scalable Image Recognition","venue":null,"work_id":"57228060-4741-4c10-b4d0-f5c5d8259302","year":2018},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.194141Z"},"links":{"citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:155fa2e0ea654b651b01756f3ed5866a57befd778b1c87ef27b76bcaf265bdb8","observation_id":"1ff1e94e-2283-4cd9-9284-3a4dcaefcb67","resolution":{"observed_at":"2026-08-14T14:06:27.615744Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1604.02201","last_updated":"2016-04-08T00:16:35Z","snapshot_observed_at":"2026-08-14T22:02:35.237315Z","submitted_at":"2016-04-08T00:16:35Z","title":"Transfer Learning for Low-Resource Neural Machine Translation","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1604.02201","snapshot_observed_at":"2026-08-14T14:06:27.198355Z","title":"Transfer learning for low-resource neural machine transla- tion","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks","version":1},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-08-14T14:06:27.198355Z"},"links":{"cited_paper":"/paper/1604.02201","citing_paper":"/paper/1908.03835"},"observation_digest":"sha256:82021ea897fc51a58cef0f1d61226c6efd804b77db72b0e14c09d2a29c77399f","observation_id":"26c3f15d-d3d4-454f-8cf7-d716a556fcea","resolution":{"observed_at":"2026-08-14T14:06:27.198355Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"1908.03835","last_updated":"2019-08-11T00:52:30Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-15T09:33:08.680895Z","submitted_at":"2019-08-11T00:52:30Z","title":"AutoGAN: Neural Architecture Search for Generative Adversarial Networks"},"reference_resolution":{"displayed":73,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":38,"verified_exact":3,"verified_fuzzy":32},"total_outbound_references":73},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 73 of 73 outbound references and 0 inbound Pith citation observations for arXiv:1908.03835."}