{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:C7FKLCBGPFHXQ4NEH7ISI6Z5PQ","short_pith_number":"pith:C7FKLCBG","schema_version":"1.0","canonical_sha256":"17caa58826794f7871a43fd1247b3d7c0d6ae393c36e7407fed57ce4a670fc67","source":{"kind":"arxiv","id":"1601.04589","version":1},"attestation_state":"computed","paper":{"title":"Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chuan Li, Michael Wand","submitted_at":"2016-01-18T16:31:37Z","abstract_excerpt":"This paper studies a combination of generative Markov random field (MRF) models and discriminatively trained deep convolutional neural networks (dCNNs) for synthesizing 2D images. The generative MRF acts on higher-levels of a dCNN feature pyramid, controling the image layout at an abstract level. We apply the method to both photographic and non-photo-realistic (artwork) synthesis tasks. The MRF regularizer prevents over-excitation artifacts and reduces implausible feature mixtures common to previous dCNN inversion approaches, permitting synthezing photographic content with increased visual pla"},"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":"1601.04589","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-01-18T16:31:37Z","cross_cats_sorted":[],"title_canon_sha256":"0d9e83d1274ea9e607cfff0d0b0d0ac9ac531bcd5d36a449059cc81d09b06d20","abstract_canon_sha256":"8dad82b8b6dea98b85fa97b0d1b9219bcd5cb677ba1ee81bc71856d506b4f7a7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:22:44.679361Z","signature_b64":"Nl3lESM35nDoFdYsZq8CmCXvEJkxpVesdtqJDMAHGOyuwFDAGKzI1W7CZf+dTONekTcUk7D/PhsICbBHkvY2AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"17caa58826794f7871a43fd1247b3d7c0d6ae393c36e7407fed57ce4a670fc67","last_reissued_at":"2026-05-18T01:22:44.678854Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:22:44.678854Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chuan Li, Michael Wand","submitted_at":"2016-01-18T16:31:37Z","abstract_excerpt":"This paper studies a combination of generative Markov random field (MRF) models and discriminatively trained deep convolutional neural networks (dCNNs) for synthesizing 2D images. The generative MRF acts on higher-levels of a dCNN feature pyramid, controling the image layout at an abstract level. We apply the method to both photographic and non-photo-realistic (artwork) synthesis tasks. The MRF regularizer prevents over-excitation artifacts and reduces implausible feature mixtures common to previous dCNN inversion approaches, permitting synthezing photographic content with increased visual pla"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1601.04589","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":""},"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":"1601.04589","created_at":"2026-05-18T01:22:44.678937+00:00"},{"alias_kind":"arxiv_version","alias_value":"1601.04589v1","created_at":"2026-05-18T01:22:44.678937+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1601.04589","created_at":"2026-05-18T01:22:44.678937+00:00"},{"alias_kind":"pith_short_12","alias_value":"C7FKLCBGPFHX","created_at":"2026-05-18T12:30:09.641336+00:00"},{"alias_kind":"pith_short_16","alias_value":"C7FKLCBGPFHXQ4NE","created_at":"2026-05-18T12:30:09.641336+00:00"},{"alias_kind":"pith_short_8","alias_value":"C7FKLCBG","created_at":"2026-05-18T12:30:09.641336+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/C7FKLCBGPFHXQ4NEH7ISI6Z5PQ","json":"https://pith.science/pith/C7FKLCBGPFHXQ4NEH7ISI6Z5PQ.json","graph_json":"https://pith.science/api/pith-number/C7FKLCBGPFHXQ4NEH7ISI6Z5PQ/graph.json","events_json":"https://pith.science/api/pith-number/C7FKLCBGPFHXQ4NEH7ISI6Z5PQ/events.json","paper":"https://pith.science/paper/C7FKLCBG"},"agent_actions":{"view_html":"https://pith.science/pith/C7FKLCBGPFHXQ4NEH7ISI6Z5PQ","download_json":"https://pith.science/pith/C7FKLCBGPFHXQ4NEH7ISI6Z5PQ.json","view_paper":"https://pith.science/paper/C7FKLCBG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1601.04589&json=true","fetch_graph":"https://pith.science/api/pith-number/C7FKLCBGPFHXQ4NEH7ISI6Z5PQ/graph.json","fetch_events":"https://pith.science/api/pith-number/C7FKLCBGPFHXQ4NEH7ISI6Z5PQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/C7FKLCBGPFHXQ4NEH7ISI6Z5PQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/C7FKLCBGPFHXQ4NEH7ISI6Z5PQ/action/storage_attestation","attest_author":"https://pith.science/pith/C7FKLCBGPFHXQ4NEH7ISI6Z5PQ/action/author_attestation","sign_citation":"https://pith.science/pith/C7FKLCBGPFHXQ4NEH7ISI6Z5PQ/action/citation_signature","submit_replication":"https://pith.science/pith/C7FKLCBGPFHXQ4NEH7ISI6Z5PQ/action/replication_record"}},"created_at":"2026-05-18T01:22:44.678937+00:00","updated_at":"2026-05-18T01:22:44.678937+00:00"}