{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:5BGG3YNVYLB6IF27UCS5ULODFD","short_pith_number":"pith:5BGG3YNV","schema_version":"1.0","canonical_sha256":"e84c6de1b5c2c3e4175fa0a5da2dc328e4bedc4eee73991e72ac040243ce6d85","source":{"kind":"arxiv","id":"2201.04684","version":1},"attestation_state":"computed","paper":{"title":"BigDatasetGAN: Synthesizing ImageNet with Pixel-wise Annotations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adela Barriuso, Antonio Torralba, Daiqing Li, Huan Ling, Karsten Kreis, Sanja Fidler, Seung Wook Kim","submitted_at":"2022-01-12T20:28:34Z","abstract_excerpt":"Annotating images with pixel-wise labels is a time-consuming and costly process. Recently, DatasetGAN showcased a promising alternative - to synthesize a large labeled dataset via a generative adversarial network (GAN) by exploiting a small set of manually labeled, GAN-generated images. Here, we scale DatasetGAN to ImageNet scale of class diversity. We take image samples from the class-conditional generative model BigGAN trained on ImageNet, and manually annotate 5 images per class, for all 1k classes. By training an effective feature segmentation architecture on top of BigGAN, we turn BigGAN "},"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":"2201.04684","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-01-12T20:28:34Z","cross_cats_sorted":[],"title_canon_sha256":"14b43f5b609661d3f85ddf257ab9003bb8480d972379a62e777c1cd5bf7f65fa","abstract_canon_sha256":"7ffa5717354b228b0d44d52038562339cbf27635f8178bd2fe29df15ed23d0fc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:48:11.648329Z","signature_b64":"E5UJDtx7oP3/hYT1X4RS0cFJnhDakfeQE3xMzksrsidY0zqhrL4gkw6CqfjMJhLKggVLIcq46V+M2RED0Pa9DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e84c6de1b5c2c3e4175fa0a5da2dc328e4bedc4eee73991e72ac040243ce6d85","last_reissued_at":"2026-07-05T03:48:11.647944Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:48:11.647944Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"BigDatasetGAN: Synthesizing ImageNet with Pixel-wise Annotations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Adela Barriuso, Antonio Torralba, Daiqing Li, Huan Ling, Karsten Kreis, Sanja Fidler, Seung Wook Kim","submitted_at":"2022-01-12T20:28:34Z","abstract_excerpt":"Annotating images with pixel-wise labels is a time-consuming and costly process. Recently, DatasetGAN showcased a promising alternative - to synthesize a large labeled dataset via a generative adversarial network (GAN) by exploiting a small set of manually labeled, GAN-generated images. Here, we scale DatasetGAN to ImageNet scale of class diversity. We take image samples from the class-conditional generative model BigGAN trained on ImageNet, and manually annotate 5 images per class, for all 1k classes. By training an effective feature segmentation architecture on top of BigGAN, we turn BigGAN "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.04684","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/2201.04684/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":"2201.04684","created_at":"2026-07-05T03:48:11.648001+00:00"},{"alias_kind":"arxiv_version","alias_value":"2201.04684v1","created_at":"2026-07-05T03:48:11.648001+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.04684","created_at":"2026-07-05T03:48:11.648001+00:00"},{"alias_kind":"pith_short_12","alias_value":"5BGG3YNVYLB6","created_at":"2026-07-05T03:48:11.648001+00:00"},{"alias_kind":"pith_short_16","alias_value":"5BGG3YNVYLB6IF27","created_at":"2026-07-05T03:48:11.648001+00:00"},{"alias_kind":"pith_short_8","alias_value":"5BGG3YNV","created_at":"2026-07-05T03:48:11.648001+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/5BGG3YNVYLB6IF27UCS5ULODFD","json":"https://pith.science/pith/5BGG3YNVYLB6IF27UCS5ULODFD.json","graph_json":"https://pith.science/api/pith-number/5BGG3YNVYLB6IF27UCS5ULODFD/graph.json","events_json":"https://pith.science/api/pith-number/5BGG3YNVYLB6IF27UCS5ULODFD/events.json","paper":"https://pith.science/paper/5BGG3YNV"},"agent_actions":{"view_html":"https://pith.science/pith/5BGG3YNVYLB6IF27UCS5ULODFD","download_json":"https://pith.science/pith/5BGG3YNVYLB6IF27UCS5ULODFD.json","view_paper":"https://pith.science/paper/5BGG3YNV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2201.04684&json=true","fetch_graph":"https://pith.science/api/pith-number/5BGG3YNVYLB6IF27UCS5ULODFD/graph.json","fetch_events":"https://pith.science/api/pith-number/5BGG3YNVYLB6IF27UCS5ULODFD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5BGG3YNVYLB6IF27UCS5ULODFD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5BGG3YNVYLB6IF27UCS5ULODFD/action/storage_attestation","attest_author":"https://pith.science/pith/5BGG3YNVYLB6IF27UCS5ULODFD/action/author_attestation","sign_citation":"https://pith.science/pith/5BGG3YNVYLB6IF27UCS5ULODFD/action/citation_signature","submit_replication":"https://pith.science/pith/5BGG3YNVYLB6IF27UCS5ULODFD/action/replication_record"}},"created_at":"2026-07-05T03:48:11.648001+00:00","updated_at":"2026-07-05T03:48:11.648001+00:00"}