{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:6MYNV4OKSOX6RQUDQ3I2BLOZMX","short_pith_number":"pith:6MYNV4OK","canonical_record":{"source":{"id":"1807.07560","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-19T17:57:16Z","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"title_canon_sha256":"90e770f5b1abb4c22dfe4d7b85ee1a6ae29743ca61ebdd76eb0c3a2323b98388","abstract_canon_sha256":"550b10dd106a03dc4ff52975200e65f70289ec4f436e2b32114c9d742914ad68"},"schema_version":"1.0"},"canonical_sha256":"f330daf1ca93afe8c28386d1a0add965d0af38e2ef6909649eacc8e8f856d834","source":{"kind":"arxiv","id":"1807.07560","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.07560","created_at":"2026-05-17T23:49:57Z"},{"alias_kind":"arxiv_version","alias_value":"1807.07560v3","created_at":"2026-05-17T23:49:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.07560","created_at":"2026-05-17T23:49:57Z"},{"alias_kind":"pith_short_12","alias_value":"6MYNV4OKSOX6","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"6MYNV4OKSOX6RQUD","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"6MYNV4OK","created_at":"2026-05-18T12:32:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:6MYNV4OKSOX6RQUDQ3I2BLOZMX","target":"record","payload":{"canonical_record":{"source":{"id":"1807.07560","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-19T17:57:16Z","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"title_canon_sha256":"90e770f5b1abb4c22dfe4d7b85ee1a6ae29743ca61ebdd76eb0c3a2323b98388","abstract_canon_sha256":"550b10dd106a03dc4ff52975200e65f70289ec4f436e2b32114c9d742914ad68"},"schema_version":"1.0"},"canonical_sha256":"f330daf1ca93afe8c28386d1a0add965d0af38e2ef6909649eacc8e8f856d834","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:57.143709Z","signature_b64":"e8FA9I4dYT8jc2ErPcoMRWXLOSJsDWlt3VgtrVB6c2FqzMGujXodTzotaIqcyLRXHNOHJD/Ez8+zKGiCe0CxCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f330daf1ca93afe8c28386d1a0add965d0af38e2ef6909649eacc8e8f856d834","last_reissued_at":"2026-05-17T23:49:57.143166Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:57.143166Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1807.07560","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:49:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iZlTsa2JWHimqlnOELmFToxV52hzL3mBJjdHoVa9ZgJQoXvd9jfv0kA/LkyOJ0bz2FU1QRQGSRTtGdJ49nTsCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T23:16:08.296664Z"},"content_sha256":"47dcd37a12f94becf6480d3c2f44da51688935f7065b55e77f7c8659f8420f4c","schema_version":"1.0","event_id":"sha256:47dcd37a12f94becf6480d3c2f44da51688935f7065b55e77f7c8659f8420f4c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:6MYNV4OKSOX6RQUDQ3I2BLOZMX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Compositional GAN: Learning Image-Conditional Binary Composition","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Deepak Pathak, Samaneh Azadi, Sayna Ebrahimi, Trevor Darrell","submitted_at":"2018-07-19T17:57:16Z","abstract_excerpt":"Generative Adversarial Networks (GANs) can produce images of remarkable complexity and realism but are generally structured to sample from a single latent source ignoring the explicit spatial interaction between multiple entities that could be present in a scene. Capturing such complex interactions between different objects in the world, including their relative scaling, spatial layout, occlusion, or viewpoint transformation is a challenging problem. In this work, we propose a novel self-consistent Composition-by-Decomposition (CoDe) network to compose a pair of objects. Given object images fr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.07560","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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:49:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"k/mohSis0ujj/tMJuu9ERLgj4nv00K1cl3a1i/h8BVO90GdWnTHGI26o2bWdolbg4LPopmgzNR7VhH3ciBFzBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-11T23:16:08.297422Z"},"content_sha256":"efdd6f3853dc31ed05b7848374b393e29b8f8d819bdf2068465728f0ec4ff813","schema_version":"1.0","event_id":"sha256:efdd6f3853dc31ed05b7848374b393e29b8f8d819bdf2068465728f0ec4ff813"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6MYNV4OKSOX6RQUDQ3I2BLOZMX/bundle.json","state_url":"https://pith.science/pith/6MYNV4OKSOX6RQUDQ3I2BLOZMX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6MYNV4OKSOX6RQUDQ3I2BLOZMX/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":"2026-06-11T23:16:08Z","links":{"resolver":"https://pith.science/pith/6MYNV4OKSOX6RQUDQ3I2BLOZMX","bundle":"https://pith.science/pith/6MYNV4OKSOX6RQUDQ3I2BLOZMX/bundle.json","state":"https://pith.science/pith/6MYNV4OKSOX6RQUDQ3I2BLOZMX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6MYNV4OKSOX6RQUDQ3I2BLOZMX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:6MYNV4OKSOX6RQUDQ3I2BLOZMX","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":"550b10dd106a03dc4ff52975200e65f70289ec4f436e2b32114c9d742914ad68","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-19T17:57:16Z","title_canon_sha256":"90e770f5b1abb4c22dfe4d7b85ee1a6ae29743ca61ebdd76eb0c3a2323b98388"},"schema_version":"1.0","source":{"id":"1807.07560","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1807.07560","created_at":"2026-05-17T23:49:57Z"},{"alias_kind":"arxiv_version","alias_value":"1807.07560v3","created_at":"2026-05-17T23:49:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1807.07560","created_at":"2026-05-17T23:49:57Z"},{"alias_kind":"pith_short_12","alias_value":"6MYNV4OKSOX6","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_16","alias_value":"6MYNV4OKSOX6RQUD","created_at":"2026-05-18T12:32:11Z"},{"alias_kind":"pith_short_8","alias_value":"6MYNV4OK","created_at":"2026-05-18T12:32:11Z"}],"graph_snapshots":[{"event_id":"sha256:efdd6f3853dc31ed05b7848374b393e29b8f8d819bdf2068465728f0ec4ff813","target":"graph","created_at":"2026-05-17T23:49:57Z","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":"Generative Adversarial Networks (GANs) can produce images of remarkable complexity and realism but are generally structured to sample from a single latent source ignoring the explicit spatial interaction between multiple entities that could be present in a scene. Capturing such complex interactions between different objects in the world, including their relative scaling, spatial layout, occlusion, or viewpoint transformation is a challenging problem. In this work, we propose a novel self-consistent Composition-by-Decomposition (CoDe) network to compose a pair of objects. Given object images fr","authors_text":"Deepak Pathak, Samaneh Azadi, Sayna Ebrahimi, Trevor Darrell","cross_cats":["cs.AI","cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-19T17:57:16Z","title":"Compositional GAN: Learning Image-Conditional Binary Composition"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1807.07560","kind":"arxiv","version":3},"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":"sha256:47dcd37a12f94becf6480d3c2f44da51688935f7065b55e77f7c8659f8420f4c","target":"record","created_at":"2026-05-17T23:49:57Z","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":"550b10dd106a03dc4ff52975200e65f70289ec4f436e2b32114c9d742914ad68","cross_cats_sorted":["cs.AI","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-07-19T17:57:16Z","title_canon_sha256":"90e770f5b1abb4c22dfe4d7b85ee1a6ae29743ca61ebdd76eb0c3a2323b98388"},"schema_version":"1.0","source":{"id":"1807.07560","kind":"arxiv","version":3}},"canonical_sha256":"f330daf1ca93afe8c28386d1a0add965d0af38e2ef6909649eacc8e8f856d834","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f330daf1ca93afe8c28386d1a0add965d0af38e2ef6909649eacc8e8f856d834","first_computed_at":"2026-05-17T23:49:57.143166Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:49:57.143166Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"e8FA9I4dYT8jc2ErPcoMRWXLOSJsDWlt3VgtrVB6c2FqzMGujXodTzotaIqcyLRXHNOHJD/Ez8+zKGiCe0CxCw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:49:57.143709Z","signed_message":"canonical_sha256_bytes"},"source_id":"1807.07560","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:47dcd37a12f94becf6480d3c2f44da51688935f7065b55e77f7c8659f8420f4c","sha256:efdd6f3853dc31ed05b7848374b393e29b8f8d819bdf2068465728f0ec4ff813"],"state_sha256":"7f02338b22ba3a88c06d288bb1c59e35d18ded402606439428439e0029aa4180"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"d8xCz8kOKhgmpGpppWSzYFXBjRTa7/9RUEQUYKOg6jOw2hxv4bAVnuE8pvlWnqn1Mk/q54UsEgveBUkYh7F8Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-11T23:16:08.300527Z","bundle_sha256":"197ed88bc8e7b029149471e7e0a19c23e5cf2a70f16155d94c7f3772e7968319"}}