{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:ICZU3OABR2XECAGZF42M7MNBHF","short_pith_number":"pith:ICZU3OAB","schema_version":"1.0","canonical_sha256":"40b34db8018eae4100d92f34cfb1a1397108d3c398de684dcb5d9c8ebafc1279","source":{"kind":"arxiv","id":"1907.10830","version":4},"attestation_state":"computed","paper":{"title":"U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Hyeonwoo Kang, Junho Kim, Kwanghee Lee, Minjae Kim","submitted_at":"2019-07-25T04:17:25Z","abstract_excerpt":"We propose a novel method for unsupervised image-to-image translation, which incorporates a new attention module and a new learnable normalization function in an end-to-end manner. The attention module guides our model to focus on more important regions distinguishing between source and target domains based on the attention map obtained by the auxiliary classifier. Unlike previous attention-based method which cannot handle the geometric changes between domains, our model can translate both images requiring holistic changes and images requiring large shape changes. Moreover, our new AdaLIN (Ada"},"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":"1907.10830","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-07-25T04:17:25Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"7ed11613de709216a04abd69bc7de1bfde161708c320cbb31694a9979d294979","abstract_canon_sha256":"1f9dfb9baae4b56a95ce681d69206ff494c91dc3f62842409700ed7bf4b3b4ab"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:53:50.768630Z","signature_b64":"d86yUOVdiKoOkvy+qMjhQfR5u6MkM2nNto6WdKbYrrR2kBmFnigPwXIRtcvf3pKj16L4Zh9y1YujdjoT3YUnAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"40b34db8018eae4100d92f34cfb1a1397108d3c398de684dcb5d9c8ebafc1279","last_reissued_at":"2026-07-05T00:53:50.768184Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:53:50.768184Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Hyeonwoo Kang, Junho Kim, Kwanghee Lee, Minjae Kim","submitted_at":"2019-07-25T04:17:25Z","abstract_excerpt":"We propose a novel method for unsupervised image-to-image translation, which incorporates a new attention module and a new learnable normalization function in an end-to-end manner. The attention module guides our model to focus on more important regions distinguishing between source and target domains based on the attention map obtained by the auxiliary classifier. Unlike previous attention-based method which cannot handle the geometric changes between domains, our model can translate both images requiring holistic changes and images requiring large shape changes. Moreover, our new AdaLIN (Ada"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1907.10830","kind":"arxiv","version":4},"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/1907.10830/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":"1907.10830","created_at":"2026-07-05T00:53:50.768239+00:00"},{"alias_kind":"arxiv_version","alias_value":"1907.10830v4","created_at":"2026-07-05T00:53:50.768239+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1907.10830","created_at":"2026-07-05T00:53:50.768239+00:00"},{"alias_kind":"pith_short_12","alias_value":"ICZU3OABR2XE","created_at":"2026-07-05T00:53:50.768239+00:00"},{"alias_kind":"pith_short_16","alias_value":"ICZU3OABR2XECAGZ","created_at":"2026-07-05T00:53:50.768239+00:00"},{"alias_kind":"pith_short_8","alias_value":"ICZU3OAB","created_at":"2026-07-05T00:53:50.768239+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.29319","citing_title":"FDM-MFVT: Few-step Sampling Diffusion Model for Mask-Free Virtual Try-On","ref_index":25,"is_internal_anchor":false},{"citing_arxiv_id":"2505.09831","citing_title":"IMPLICITSTAINER: Resolution Agnostic Data-Efficient Virtual Staining Using Neural Implicit Functions","ref_index":23,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ICZU3OABR2XECAGZF42M7MNBHF","json":"https://pith.science/pith/ICZU3OABR2XECAGZF42M7MNBHF.json","graph_json":"https://pith.science/api/pith-number/ICZU3OABR2XECAGZF42M7MNBHF/graph.json","events_json":"https://pith.science/api/pith-number/ICZU3OABR2XECAGZF42M7MNBHF/events.json","paper":"https://pith.science/paper/ICZU3OAB"},"agent_actions":{"view_html":"https://pith.science/pith/ICZU3OABR2XECAGZF42M7MNBHF","download_json":"https://pith.science/pith/ICZU3OABR2XECAGZF42M7MNBHF.json","view_paper":"https://pith.science/paper/ICZU3OAB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1907.10830&json=true","fetch_graph":"https://pith.science/api/pith-number/ICZU3OABR2XECAGZF42M7MNBHF/graph.json","fetch_events":"https://pith.science/api/pith-number/ICZU3OABR2XECAGZF42M7MNBHF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ICZU3OABR2XECAGZF42M7MNBHF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ICZU3OABR2XECAGZF42M7MNBHF/action/storage_attestation","attest_author":"https://pith.science/pith/ICZU3OABR2XECAGZF42M7MNBHF/action/author_attestation","sign_citation":"https://pith.science/pith/ICZU3OABR2XECAGZF42M7MNBHF/action/citation_signature","submit_replication":"https://pith.science/pith/ICZU3OABR2XECAGZF42M7MNBHF/action/replication_record"}},"created_at":"2026-07-05T00:53:50.768239+00:00","updated_at":"2026-07-05T00:53:50.768239+00:00"}