{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:3KMYQQKXPOSB62VOLGAHYZEMP5","short_pith_number":"pith:3KMYQQKX","schema_version":"1.0","canonical_sha256":"da998841577ba41f6aae59807c648c7f5d7dac0149cd4dbaac46560f9f44599b","source":{"kind":"arxiv","id":"1611.07004","version":3},"attestation_state":"computed","paper":{"title":"Image-to-Image Translation with Conditional Adversarial Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexei A. Efros, Jun-Yan Zhu, Phillip Isola, Tinghui Zhou","submitted_at":"2016-11-21T20:48:16Z","abstract_excerpt":"We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Indeed, since the release of the pix2pix softwa"},"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":"1611.07004","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-11-21T20:48:16Z","cross_cats_sorted":[],"title_canon_sha256":"657abac962bf83061e7aad2fc6eaf9893faadd9bf636760ca4927bd5311abbac","abstract_canon_sha256":"13d25710e4f561f75a42a9acbae4b8387c05092985c5cc904d298f5e1e2d03e6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:59:58.312539Z","signature_b64":"uQof6/fxuGqoLV9AQik5L7EgePEmqIGZNeHDx3Kn248tik3Tm53B2zqPQKwcshbtFeESKGcCbWjZq3LDiNvPCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"da998841577ba41f6aae59807c648c7f5d7dac0149cd4dbaac46560f9f44599b","last_reissued_at":"2026-05-17T23:59:58.311971Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:59:58.311971Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Image-to-Image Translation with Conditional Adversarial Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alexei A. Efros, Jun-Yan Zhu, Phillip Isola, Tinghui Zhou","submitted_at":"2016-11-21T20:48:16Z","abstract_excerpt":"We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. Indeed, since the release of the pix2pix softwa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1611.07004","kind":"arxiv","version":3},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1611.07004","created_at":"2026-05-17T23:59:58.312038+00:00"},{"alias_kind":"arxiv_version","alias_value":"1611.07004v3","created_at":"2026-05-17T23:59:58.312038+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1611.07004","created_at":"2026-05-17T23:59:58.312038+00:00"},{"alias_kind":"pith_short_12","alias_value":"3KMYQQKXPOSB","created_at":"2026-05-18T12:29:55.572404+00:00"},{"alias_kind":"pith_short_16","alias_value":"3KMYQQKXPOSB62VO","created_at":"2026-05-18T12:29:55.572404+00:00"},{"alias_kind":"pith_short_8","alias_value":"3KMYQQKX","created_at":"2026-05-18T12:29:55.572404+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":11,"internal_anchor_count":3,"sample":[{"citing_arxiv_id":"2605.20064","citing_title":"Cardiac fat segmentation using computed tomography and an image-to-image conditional generative adversarial neural network","ref_index":11,"is_internal_anchor":true},{"citing_arxiv_id":"2605.16476","citing_title":"Deep Learning for MRI Slice Interpolation: The Critical Role of Problem Formulation","ref_index":6,"is_internal_anchor":true},{"citing_arxiv_id":"2511.06731","citing_title":"Recovering Sub-threshold S-wave Arrivals in Deep Learning Phase Pickers via Shape-Aware Loss","ref_index":8,"is_internal_anchor":true},{"citing_arxiv_id":"2605.08282","citing_title":"A Paired Point-of-Care Ultrasound Dataset for Image Quality Enhancement and Benchmarking via a cGAN Baseline","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2604.18251","citing_title":"Style-Based Neural Architectures for Real-Time Weather Classification","ref_index":1,"is_internal_anchor":false},{"citing_arxiv_id":"2604.10700","citing_title":"VCC-DSA: A Novel Vascular Consistency Constrained DSA Imaging Model for Motion Artifact Suppression","ref_index":4,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06678","citing_title":"A Wasserstein GAN-based climate scenario generator for risk management and insurance: the case of soil subsidence","ref_index":26,"is_internal_anchor":false},{"citing_arxiv_id":"2604.13947","citing_title":"Heuristic Style Transfer for Real-Time, Efficient Weather Attribute Detection","ref_index":47,"is_internal_anchor":false},{"citing_arxiv_id":"2604.16086","citing_title":"Stylistic-STORM (ST-STORM) : Perceiving the Semantic Nature of Appearance","ref_index":36,"is_internal_anchor":false},{"citing_arxiv_id":"2604.21801","citing_title":"SyMTRS: Benchmark Multi-Task Synthetic Dataset for Depth, Domain Adaptation and Super-Resolution in Aerial Imagery","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2604.23799","citing_title":"VitaminP: cross-modal learning enables whole-cell segmentation from routine histology","ref_index":30,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3KMYQQKXPOSB62VOLGAHYZEMP5","json":"https://pith.science/pith/3KMYQQKXPOSB62VOLGAHYZEMP5.json","graph_json":"https://pith.science/api/pith-number/3KMYQQKXPOSB62VOLGAHYZEMP5/graph.json","events_json":"https://pith.science/api/pith-number/3KMYQQKXPOSB62VOLGAHYZEMP5/events.json","paper":"https://pith.science/paper/3KMYQQKX"},"agent_actions":{"view_html":"https://pith.science/pith/3KMYQQKXPOSB62VOLGAHYZEMP5","download_json":"https://pith.science/pith/3KMYQQKXPOSB62VOLGAHYZEMP5.json","view_paper":"https://pith.science/paper/3KMYQQKX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1611.07004&json=true","fetch_graph":"https://pith.science/api/pith-number/3KMYQQKXPOSB62VOLGAHYZEMP5/graph.json","fetch_events":"https://pith.science/api/pith-number/3KMYQQKXPOSB62VOLGAHYZEMP5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3KMYQQKXPOSB62VOLGAHYZEMP5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3KMYQQKXPOSB62VOLGAHYZEMP5/action/storage_attestation","attest_author":"https://pith.science/pith/3KMYQQKXPOSB62VOLGAHYZEMP5/action/author_attestation","sign_citation":"https://pith.science/pith/3KMYQQKXPOSB62VOLGAHYZEMP5/action/citation_signature","submit_replication":"https://pith.science/pith/3KMYQQKXPOSB62VOLGAHYZEMP5/action/replication_record"}},"created_at":"2026-05-17T23:59:58.312038+00:00","updated_at":"2026-05-17T23:59:58.312038+00:00"}