{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:TXFISYFNWFT5TXOUT4IJEDQ4WX","short_pith_number":"pith:TXFISYFN","schema_version":"1.0","canonical_sha256":"9dca8960adb167d9ddd49f10920e1cb5c0b8028812901db9180254d688a1d5e1","source":{"kind":"arxiv","id":"1612.02844","version":1},"attestation_state":"computed","paper":{"title":"Deep TEN: Texture Encoding Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hang Zhang, Jia Xue, Kristin Dana","submitted_at":"2016-12-08T21:27:31Z","abstract_excerpt":"We propose a Deep Texture Encoding Network (Deep-TEN) with a novel Encoding Layer integrated on top of convolutional layers, which ports the entire dictionary learning and encoding pipeline into a single model. Current methods build from distinct components, using standard encoders with separate off-the-shelf features such as SIFT descriptors or pre-trained CNN features for material recognition. Our new approach provides an end-to-end learning framework, where the inherent visual vocabularies are learned directly from the loss function. The features, dictionaries and the encoding representatio"},"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":"1612.02844","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2016-12-08T21:27:31Z","cross_cats_sorted":[],"title_canon_sha256":"8c7034fb07e5acd45a7a6b5a7ecf01754a150593b4dff8902f7a1535beb20048","abstract_canon_sha256":"816600aa6bf43b39b3926377d2434713fa1a6b3e760e89f13dc12cc9908c79d8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:55:28.136811Z","signature_b64":"/mI6guRSqztV8LOsE+hcBXzIGX90rIbyqkIxaWU83XExSlgPU2pCfYl9pU07WRzjGCvwOurUe/C1b5S7yqPbCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9dca8960adb167d9ddd49f10920e1cb5c0b8028812901db9180254d688a1d5e1","last_reissued_at":"2026-05-18T00:55:28.136301Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:55:28.136301Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep TEN: Texture Encoding Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Hang Zhang, Jia Xue, Kristin Dana","submitted_at":"2016-12-08T21:27:31Z","abstract_excerpt":"We propose a Deep Texture Encoding Network (Deep-TEN) with a novel Encoding Layer integrated on top of convolutional layers, which ports the entire dictionary learning and encoding pipeline into a single model. Current methods build from distinct components, using standard encoders with separate off-the-shelf features such as SIFT descriptors or pre-trained CNN features for material recognition. Our new approach provides an end-to-end learning framework, where the inherent visual vocabularies are learned directly from the loss function. The features, dictionaries and the encoding representatio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1612.02844","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":"1612.02844","created_at":"2026-05-18T00:55:28.136395+00:00"},{"alias_kind":"arxiv_version","alias_value":"1612.02844v1","created_at":"2026-05-18T00:55:28.136395+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1612.02844","created_at":"2026-05-18T00:55:28.136395+00:00"},{"alias_kind":"pith_short_12","alias_value":"TXFISYFNWFT5","created_at":"2026-05-18T12:30:46.583412+00:00"},{"alias_kind":"pith_short_16","alias_value":"TXFISYFNWFT5TXOU","created_at":"2026-05-18T12:30:46.583412+00:00"},{"alias_kind":"pith_short_8","alias_value":"TXFISYFN","created_at":"2026-05-18T12:30:46.583412+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/TXFISYFNWFT5TXOUT4IJEDQ4WX","json":"https://pith.science/pith/TXFISYFNWFT5TXOUT4IJEDQ4WX.json","graph_json":"https://pith.science/api/pith-number/TXFISYFNWFT5TXOUT4IJEDQ4WX/graph.json","events_json":"https://pith.science/api/pith-number/TXFISYFNWFT5TXOUT4IJEDQ4WX/events.json","paper":"https://pith.science/paper/TXFISYFN"},"agent_actions":{"view_html":"https://pith.science/pith/TXFISYFNWFT5TXOUT4IJEDQ4WX","download_json":"https://pith.science/pith/TXFISYFNWFT5TXOUT4IJEDQ4WX.json","view_paper":"https://pith.science/paper/TXFISYFN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1612.02844&json=true","fetch_graph":"https://pith.science/api/pith-number/TXFISYFNWFT5TXOUT4IJEDQ4WX/graph.json","fetch_events":"https://pith.science/api/pith-number/TXFISYFNWFT5TXOUT4IJEDQ4WX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TXFISYFNWFT5TXOUT4IJEDQ4WX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TXFISYFNWFT5TXOUT4IJEDQ4WX/action/storage_attestation","attest_author":"https://pith.science/pith/TXFISYFNWFT5TXOUT4IJEDQ4WX/action/author_attestation","sign_citation":"https://pith.science/pith/TXFISYFNWFT5TXOUT4IJEDQ4WX/action/citation_signature","submit_replication":"https://pith.science/pith/TXFISYFNWFT5TXOUT4IJEDQ4WX/action/replication_record"}},"created_at":"2026-05-18T00:55:28.136395+00:00","updated_at":"2026-05-18T00:55:28.136395+00:00"}