{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:GTHL5NMZDYKUWJGSG626LCFQ3A","short_pith_number":"pith:GTHL5NMZ","schema_version":"1.0","canonical_sha256":"34cebeb5991e154b24d237b5e588b0d83f72dcc36d1ff98c23dfbed13b0628ab","source":{"kind":"arxiv","id":"1707.01253","version":2},"attestation_state":"computed","paper":{"title":"Laplacian-Steered Neural Style Transfer","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Liqiang Nie, Shaohua Li, Tat-Seng Chua, Xinxing Xu","submitted_at":"2017-07-05T08:10:41Z","abstract_excerpt":"Neural Style Transfer based on Convolutional Neural Networks (CNN) aims to synthesize a new image that retains the high-level structure of a content image, rendered in the low-level texture of a style image. This is achieved by constraining the new image to have high-level CNN features similar to the content image, and lower-level CNN features similar to the style image. However in the traditional optimization objective, low-level features of the content image are absent, and the low-level features of the style image dominate the low-level detail structures of the new image. Hence in the synth"},"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":"1707.01253","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2017-07-05T08:10:41Z","cross_cats_sorted":[],"title_canon_sha256":"640c19065ab74c29b4536c7aff1fb6dc43eab0c25af1c2025e28e56edf30cc0a","abstract_canon_sha256":"ebb7f38f6e290712011bad2caf6f0f0c25c8b012b9d2e6caa493fd7a2616d739"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:39:11.957280Z","signature_b64":"RkZJrv3tDA07s8tFQXpBElU12lIgVG1WGlOMnTXz2vI63gGCb7TWpgF0E9W4OhwQ10dpKdFTHw8jOMBNWqDwDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"34cebeb5991e154b24d237b5e588b0d83f72dcc36d1ff98c23dfbed13b0628ab","last_reissued_at":"2026-05-18T00:39:11.956428Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:39:11.956428Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Laplacian-Steered Neural Style Transfer","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Liqiang Nie, Shaohua Li, Tat-Seng Chua, Xinxing Xu","submitted_at":"2017-07-05T08:10:41Z","abstract_excerpt":"Neural Style Transfer based on Convolutional Neural Networks (CNN) aims to synthesize a new image that retains the high-level structure of a content image, rendered in the low-level texture of a style image. This is achieved by constraining the new image to have high-level CNN features similar to the content image, and lower-level CNN features similar to the style image. However in the traditional optimization objective, low-level features of the content image are absent, and the low-level features of the style image dominate the low-level detail structures of the new image. Hence in the synth"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.01253","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":"1707.01253","created_at":"2026-05-18T00:39:11.956577+00:00"},{"alias_kind":"arxiv_version","alias_value":"1707.01253v2","created_at":"2026-05-18T00:39:11.956577+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.01253","created_at":"2026-05-18T00:39:11.956577+00:00"},{"alias_kind":"pith_short_12","alias_value":"GTHL5NMZDYKU","created_at":"2026-05-18T12:31:18.294218+00:00"},{"alias_kind":"pith_short_16","alias_value":"GTHL5NMZDYKUWJGS","created_at":"2026-05-18T12:31:18.294218+00:00"},{"alias_kind":"pith_short_8","alias_value":"GTHL5NMZ","created_at":"2026-05-18T12:31:18.294218+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/GTHL5NMZDYKUWJGSG626LCFQ3A","json":"https://pith.science/pith/GTHL5NMZDYKUWJGSG626LCFQ3A.json","graph_json":"https://pith.science/api/pith-number/GTHL5NMZDYKUWJGSG626LCFQ3A/graph.json","events_json":"https://pith.science/api/pith-number/GTHL5NMZDYKUWJGSG626LCFQ3A/events.json","paper":"https://pith.science/paper/GTHL5NMZ"},"agent_actions":{"view_html":"https://pith.science/pith/GTHL5NMZDYKUWJGSG626LCFQ3A","download_json":"https://pith.science/pith/GTHL5NMZDYKUWJGSG626LCFQ3A.json","view_paper":"https://pith.science/paper/GTHL5NMZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1707.01253&json=true","fetch_graph":"https://pith.science/api/pith-number/GTHL5NMZDYKUWJGSG626LCFQ3A/graph.json","fetch_events":"https://pith.science/api/pith-number/GTHL5NMZDYKUWJGSG626LCFQ3A/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GTHL5NMZDYKUWJGSG626LCFQ3A/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GTHL5NMZDYKUWJGSG626LCFQ3A/action/storage_attestation","attest_author":"https://pith.science/pith/GTHL5NMZDYKUWJGSG626LCFQ3A/action/author_attestation","sign_citation":"https://pith.science/pith/GTHL5NMZDYKUWJGSG626LCFQ3A/action/citation_signature","submit_replication":"https://pith.science/pith/GTHL5NMZDYKUWJGSG626LCFQ3A/action/replication_record"}},"created_at":"2026-05-18T00:39:11.956577+00:00","updated_at":"2026-05-18T00:39:11.956577+00:00"}