{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:2ET2FXCPLSJAIFQ2XAKGRYUUBH","short_pith_number":"pith:2ET2FXCP","canonical_record":{"source":{"id":"1706.02071","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-07T07:27:20Z","cross_cats_sorted":[],"title_canon_sha256":"6aeb76fb93332a76da625ec9933f201a51dea12a48436a60ebe66828e6fef9f3","abstract_canon_sha256":"cfa968cd597f539f9e2dfaccda6caa91b6a973858813994ef2946b6dde6f3a34"},"schema_version":"1.0"},"canonical_sha256":"d127a2dc4f5c9204161ab81468e29409f27cbe49d268dd089391c427d8ff3428","source":{"kind":"arxiv","id":"1706.02071","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.02071","created_at":"2026-05-18T00:42:50Z"},{"alias_kind":"arxiv_version","alias_value":"1706.02071v1","created_at":"2026-05-18T00:42:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.02071","created_at":"2026-05-18T00:42:50Z"},{"alias_kind":"pith_short_12","alias_value":"2ET2FXCPLSJA","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_16","alias_value":"2ET2FXCPLSJAIFQ2","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_8","alias_value":"2ET2FXCP","created_at":"2026-05-18T12:30:55Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:2ET2FXCPLSJAIFQ2XAKGRYUUBH","target":"record","payload":{"canonical_record":{"source":{"id":"1706.02071","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-07T07:27:20Z","cross_cats_sorted":[],"title_canon_sha256":"6aeb76fb93332a76da625ec9933f201a51dea12a48436a60ebe66828e6fef9f3","abstract_canon_sha256":"cfa968cd597f539f9e2dfaccda6caa91b6a973858813994ef2946b6dde6f3a34"},"schema_version":"1.0"},"canonical_sha256":"d127a2dc4f5c9204161ab81468e29409f27cbe49d268dd089391c427d8ff3428","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:42:50.194223Z","signature_b64":"ux4JFy9HW/jCzyIN3Ooe0dynm73PKkT5uvhDjAqgOOiSgxEMORBT/0ZZDwUoQh3O4KJvoas8/1ii97yI35aDCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d127a2dc4f5c9204161ab81468e29409f27cbe49d268dd089391c427d8ff3428","last_reissued_at":"2026-05-18T00:42:50.193474Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:42:50.193474Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1706.02071","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:42:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"GmawSc7wmnsIQ/BRswzjt4jbHIRo3PQ1yOVXszagcj2Twx+DuFfXt9r2YMVjIKkZwxKHS7wsvnsAp/MVCFErCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T08:56:48.934474Z"},"content_sha256":"197c9d1c5358855787c1c2031b4379777bb4ceff0e899fdabd8cdb23ec0f6444","schema_version":"1.0","event_id":"sha256:197c9d1c5358855787c1c2031b4379777bb4ceff0e899fdabd8cdb23ec0f6444"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:2ET2FXCPLSJAIFQ2XAKGRYUUBH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ravi Kiran Sarvadevabhatla, Swaminathan Gurumurthy, Venkatesh Babu Radhakrishnan","submitted_at":"2017-06-07T07:27:20Z","abstract_excerpt":"A class of recent approaches for generating images, called Generative Adversarial Networks (GAN), have been used to generate impressively realistic images of objects, bedrooms, handwritten digits and a variety of other image modalities. However, typical GAN-based approaches require large amounts of training data to capture the diversity across the image modality. In this paper, we propose DeLiGAN -- a novel GAN-based architecture for diverse and limited training data scenarios. In our approach, we reparameterize the latent generative space as a mixture model and learn the mixture model's param"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.02071","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:42:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UfE9pp2/sVY5swTcoUSEWWEcGFWY3IbarCaVhDJICyyqpAX11HrlobmOm864QibHaFAKph2YEHocuI6sadXNBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-25T08:56:48.935214Z"},"content_sha256":"f9378187e1bec492cc4121ce93fb84c79bfc0156198170feec041f45993e19ed","schema_version":"1.0","event_id":"sha256:f9378187e1bec492cc4121ce93fb84c79bfc0156198170feec041f45993e19ed"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2ET2FXCPLSJAIFQ2XAKGRYUUBH/bundle.json","state_url":"https://pith.science/pith/2ET2FXCPLSJAIFQ2XAKGRYUUBH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2ET2FXCPLSJAIFQ2XAKGRYUUBH/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-25T08:56:48Z","links":{"resolver":"https://pith.science/pith/2ET2FXCPLSJAIFQ2XAKGRYUUBH","bundle":"https://pith.science/pith/2ET2FXCPLSJAIFQ2XAKGRYUUBH/bundle.json","state":"https://pith.science/pith/2ET2FXCPLSJAIFQ2XAKGRYUUBH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2ET2FXCPLSJAIFQ2XAKGRYUUBH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:2ET2FXCPLSJAIFQ2XAKGRYUUBH","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"cfa968cd597f539f9e2dfaccda6caa91b6a973858813994ef2946b6dde6f3a34","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-07T07:27:20Z","title_canon_sha256":"6aeb76fb93332a76da625ec9933f201a51dea12a48436a60ebe66828e6fef9f3"},"schema_version":"1.0","source":{"id":"1706.02071","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1706.02071","created_at":"2026-05-18T00:42:50Z"},{"alias_kind":"arxiv_version","alias_value":"1706.02071v1","created_at":"2026-05-18T00:42:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1706.02071","created_at":"2026-05-18T00:42:50Z"},{"alias_kind":"pith_short_12","alias_value":"2ET2FXCPLSJA","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_16","alias_value":"2ET2FXCPLSJAIFQ2","created_at":"2026-05-18T12:30:55Z"},{"alias_kind":"pith_short_8","alias_value":"2ET2FXCP","created_at":"2026-05-18T12:30:55Z"}],"graph_snapshots":[{"event_id":"sha256:f9378187e1bec492cc4121ce93fb84c79bfc0156198170feec041f45993e19ed","target":"graph","created_at":"2026-05-18T00:42:50Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"A class of recent approaches for generating images, called Generative Adversarial Networks (GAN), have been used to generate impressively realistic images of objects, bedrooms, handwritten digits and a variety of other image modalities. However, typical GAN-based approaches require large amounts of training data to capture the diversity across the image modality. In this paper, we propose DeLiGAN -- a novel GAN-based architecture for diverse and limited training data scenarios. In our approach, we reparameterize the latent generative space as a mixture model and learn the mixture model's param","authors_text":"Ravi Kiran Sarvadevabhatla, Swaminathan Gurumurthy, Venkatesh Babu Radhakrishnan","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-07T07:27:20Z","title":"DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1706.02071","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:197c9d1c5358855787c1c2031b4379777bb4ceff0e899fdabd8cdb23ec0f6444","target":"record","created_at":"2026-05-18T00:42:50Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"cfa968cd597f539f9e2dfaccda6caa91b6a973858813994ef2946b6dde6f3a34","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-06-07T07:27:20Z","title_canon_sha256":"6aeb76fb93332a76da625ec9933f201a51dea12a48436a60ebe66828e6fef9f3"},"schema_version":"1.0","source":{"id":"1706.02071","kind":"arxiv","version":1}},"canonical_sha256":"d127a2dc4f5c9204161ab81468e29409f27cbe49d268dd089391c427d8ff3428","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d127a2dc4f5c9204161ab81468e29409f27cbe49d268dd089391c427d8ff3428","first_computed_at":"2026-05-18T00:42:50.193474Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:42:50.193474Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"ux4JFy9HW/jCzyIN3Ooe0dynm73PKkT5uvhDjAqgOOiSgxEMORBT/0ZZDwUoQh3O4KJvoas8/1ii97yI35aDCQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:42:50.194223Z","signed_message":"canonical_sha256_bytes"},"source_id":"1706.02071","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:197c9d1c5358855787c1c2031b4379777bb4ceff0e899fdabd8cdb23ec0f6444","sha256:f9378187e1bec492cc4121ce93fb84c79bfc0156198170feec041f45993e19ed"],"state_sha256":"8744bfe906f45f0a5aa65ee31fc20614097a1d77646865af2402b71d41c67a70"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RL+TrJavFHD9KBHKb2pJJJ6A5Q0NXdsFJxpCFLj5ffBdmgJFo0ozLEIpvAE/4wVGnKGOMHK4RmrpLOSb5CYvDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-25T08:56:48.938829Z","bundle_sha256":"ef2410d3182fcc6075285bb191d8775bda217ba76ceef6092681541386b60cb2"}}