{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:WZUXCAOARF2UITNEMWULG6EI5G","short_pith_number":"pith:WZUXCAOA","schema_version":"1.0","canonical_sha256":"b6697101c08975444da465a8b37888e98932e8380be25dd484abe88385ef2661","source":{"kind":"arxiv","id":"1511.06362","version":2},"attestation_state":"computed","paper":{"title":"Efficient inference in occlusion-aware generative models of images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Jonathan Huang, Kevin Murphy","submitted_at":"2015-11-19T20:56:27Z","abstract_excerpt":"We present a generative model of images based on layering, in which image layers are individually generated, then composited from front to back. We are thus able to factor the appearance of an image into the appearance of individual objects within the image --- and additionally for each individual object, we can factor content from pose. Unlike prior work on layered models, we learn a shape prior for each object/layer, allowing the model to tease out which object is in front by looking for a consistent shape, without needing access to motion cues or any labeled data. We show that ordinary stoc"},"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":"1511.06362","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2015-11-19T20:56:27Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"3adeebd3535500516613ca094854f7385e8c46f03bbfd9d6467eb927a641ad86","abstract_canon_sha256":"272dfe4a1b91fbccf3b6a5db200410899be323f9f710b9db5b0d6cda85d698f5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:20:41.618330Z","signature_b64":"DHr8jxJ6BlDIEB+8HpP6hXzua5Qlc8d2pZ2NN/VDZ/OQRF25m3HCG2kVmJEX+dzJvsDeZm6tK585QhjnOfDlBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b6697101c08975444da465a8b37888e98932e8380be25dd484abe88385ef2661","last_reissued_at":"2026-05-18T01:20:41.617876Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:20:41.617876Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient inference in occlusion-aware generative models of images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"cs.LG","authors_text":"Jonathan Huang, Kevin Murphy","submitted_at":"2015-11-19T20:56:27Z","abstract_excerpt":"We present a generative model of images based on layering, in which image layers are individually generated, then composited from front to back. We are thus able to factor the appearance of an image into the appearance of individual objects within the image --- and additionally for each individual object, we can factor content from pose. Unlike prior work on layered models, we learn a shape prior for each object/layer, allowing the model to tease out which object is in front by looking for a consistent shape, without needing access to motion cues or any labeled data. We show that ordinary stoc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1511.06362","kind":"arxiv","version":2},"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":"1511.06362","created_at":"2026-05-18T01:20:41.617936+00:00"},{"alias_kind":"arxiv_version","alias_value":"1511.06362v2","created_at":"2026-05-18T01:20:41.617936+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1511.06362","created_at":"2026-05-18T01:20:41.617936+00:00"},{"alias_kind":"pith_short_12","alias_value":"WZUXCAOARF2U","created_at":"2026-05-18T12:29:47.479230+00:00"},{"alias_kind":"pith_short_16","alias_value":"WZUXCAOARF2UITNE","created_at":"2026-05-18T12:29:47.479230+00:00"},{"alias_kind":"pith_short_8","alias_value":"WZUXCAOA","created_at":"2026-05-18T12:29:47.479230+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/WZUXCAOARF2UITNEMWULG6EI5G","json":"https://pith.science/pith/WZUXCAOARF2UITNEMWULG6EI5G.json","graph_json":"https://pith.science/api/pith-number/WZUXCAOARF2UITNEMWULG6EI5G/graph.json","events_json":"https://pith.science/api/pith-number/WZUXCAOARF2UITNEMWULG6EI5G/events.json","paper":"https://pith.science/paper/WZUXCAOA"},"agent_actions":{"view_html":"https://pith.science/pith/WZUXCAOARF2UITNEMWULG6EI5G","download_json":"https://pith.science/pith/WZUXCAOARF2UITNEMWULG6EI5G.json","view_paper":"https://pith.science/paper/WZUXCAOA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1511.06362&json=true","fetch_graph":"https://pith.science/api/pith-number/WZUXCAOARF2UITNEMWULG6EI5G/graph.json","fetch_events":"https://pith.science/api/pith-number/WZUXCAOARF2UITNEMWULG6EI5G/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WZUXCAOARF2UITNEMWULG6EI5G/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WZUXCAOARF2UITNEMWULG6EI5G/action/storage_attestation","attest_author":"https://pith.science/pith/WZUXCAOARF2UITNEMWULG6EI5G/action/author_attestation","sign_citation":"https://pith.science/pith/WZUXCAOARF2UITNEMWULG6EI5G/action/citation_signature","submit_replication":"https://pith.science/pith/WZUXCAOARF2UITNEMWULG6EI5G/action/replication_record"}},"created_at":"2026-05-18T01:20:41.617936+00:00","updated_at":"2026-05-18T01:20:41.617936+00:00"}