{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:D5NEN2PKKR2OQEFLXI2C2AVGSB","short_pith_number":"pith:D5NEN2PK","schema_version":"1.0","canonical_sha256":"1f5a46e9ea5474e810abba342d02a6907975e2252e965067646de689ea9d0d77","source":{"kind":"arxiv","id":"1909.03611","version":1},"attestation_state":"computed","paper":{"title":"An Acceleration Framework for High Resolution Image Synthesis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Jianqiang Ren, Jinlin Liu, Yuan Yao","submitted_at":"2019-09-09T03:19:25Z","abstract_excerpt":"Synthesis of high resolution images using Generative Adversarial Networks (GANs) is challenging, which usually requires numbers of high-end graphic cards with large memory and long time of training. In this paper, we propose a two-stage framework to accelerate the training process of synthesizing high resolution images. High resolution images are first transformed to small codes via the trained encoder and decoder networks. The code in latent space is times smaller than the original high resolution images. Then, we train a code generation network to learn the distribution of the latent codes. "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1909.03611","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-09-09T03:19:25Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"65b829d3a4e9c272f778073fa675c3c8a35b5fe55c4f64be83e509c651d1b36d","abstract_canon_sha256":"580f99b0703d7d2849062c461cc221a3d49c9890bb2bdd1f254cf7973f68bdda"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:03:07.250015Z","signature_b64":"ms7z114MwCfF1Cgypi+iJFVh11bWWTlpV5xhNzbFikXcvR7F4qKVHVKW6HefSDB0EFOVYvXeT4S1Tbv89h3jAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1f5a46e9ea5474e810abba342d02a6907975e2252e965067646de689ea9d0d77","last_reissued_at":"2026-07-05T00:03:07.249584Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:03:07.249584Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Acceleration Framework for High Resolution Image Synthesis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Jianqiang Ren, Jinlin Liu, Yuan Yao","submitted_at":"2019-09-09T03:19:25Z","abstract_excerpt":"Synthesis of high resolution images using Generative Adversarial Networks (GANs) is challenging, which usually requires numbers of high-end graphic cards with large memory and long time of training. In this paper, we propose a two-stage framework to accelerate the training process of synthesizing high resolution images. High resolution images are first transformed to small codes via the trained encoder and decoder networks. The code in latent space is times smaller than the original high resolution images. Then, we train a code generation network to learn the distribution of the latent codes. "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.03611","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1909.03611/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1909.03611","created_at":"2026-07-05T00:03:07.249646+00:00"},{"alias_kind":"arxiv_version","alias_value":"1909.03611v1","created_at":"2026-07-05T00:03:07.249646+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.03611","created_at":"2026-07-05T00:03:07.249646+00:00"},{"alias_kind":"pith_short_12","alias_value":"D5NEN2PKKR2O","created_at":"2026-07-05T00:03:07.249646+00:00"},{"alias_kind":"pith_short_16","alias_value":"D5NEN2PKKR2OQEFL","created_at":"2026-07-05T00:03:07.249646+00:00"},{"alias_kind":"pith_short_8","alias_value":"D5NEN2PK","created_at":"2026-07-05T00:03:07.249646+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/D5NEN2PKKR2OQEFLXI2C2AVGSB","json":"https://pith.science/pith/D5NEN2PKKR2OQEFLXI2C2AVGSB.json","graph_json":"https://pith.science/api/pith-number/D5NEN2PKKR2OQEFLXI2C2AVGSB/graph.json","events_json":"https://pith.science/api/pith-number/D5NEN2PKKR2OQEFLXI2C2AVGSB/events.json","paper":"https://pith.science/paper/D5NEN2PK"},"agent_actions":{"view_html":"https://pith.science/pith/D5NEN2PKKR2OQEFLXI2C2AVGSB","download_json":"https://pith.science/pith/D5NEN2PKKR2OQEFLXI2C2AVGSB.json","view_paper":"https://pith.science/paper/D5NEN2PK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1909.03611&json=true","fetch_graph":"https://pith.science/api/pith-number/D5NEN2PKKR2OQEFLXI2C2AVGSB/graph.json","fetch_events":"https://pith.science/api/pith-number/D5NEN2PKKR2OQEFLXI2C2AVGSB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/D5NEN2PKKR2OQEFLXI2C2AVGSB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/D5NEN2PKKR2OQEFLXI2C2AVGSB/action/storage_attestation","attest_author":"https://pith.science/pith/D5NEN2PKKR2OQEFLXI2C2AVGSB/action/author_attestation","sign_citation":"https://pith.science/pith/D5NEN2PKKR2OQEFLXI2C2AVGSB/action/citation_signature","submit_replication":"https://pith.science/pith/D5NEN2PKKR2OQEFLXI2C2AVGSB/action/replication_record"}},"created_at":"2026-07-05T00:03:07.249646+00:00","updated_at":"2026-07-05T00:03:07.249646+00:00"}