{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:GOGZPWV75274DYQDSGRW65PWVR","short_pith_number":"pith:GOGZPWV7","schema_version":"1.0","canonical_sha256":"338d97dabfeebfc1e20391a36f75f6ac7740c5d8dd82155889e6f9a5a8a485ae","source":{"kind":"arxiv","id":"1805.08855","version":2},"attestation_state":"computed","paper":{"title":"Rate-Optimal Denoising with Deep Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.SP","math.IT","math.OC"],"primary_cat":"cs.IT","authors_text":"Paul Hand, Reinhard Heckel, Vladislav Voroninski, Wen Huang","submitted_at":"2018-05-22T20:33:52Z","abstract_excerpt":"Deep neural networks provide state-of-the-art performance for image denoising, where the goal is to recover a near noise-free image from a noisy observation. The underlying principle is that neural networks trained on large datasets have empirically been shown to be able to generate natural images well from a low-dimensional latent representation of the image. Given such a generator network, a noisy image can be denoised by i) finding the closest image in the range of the generator or by ii) passing it through an encoder-generator architecture (known as an autoencoder). However, there is littl"},"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":"1805.08855","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.IT","submitted_at":"2018-05-22T20:33:52Z","cross_cats_sorted":["cs.LG","eess.SP","math.IT","math.OC"],"title_canon_sha256":"9d32b35cee132d0891e42583454722ec88efcf2ff7a57b18dfd67e91c08597f9","abstract_canon_sha256":"e44df17efb9c2936c40fa5af7e5fbc42f495b3dab109db17bcfab81609abfb74"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:17.648519Z","signature_b64":"otFVonDnSm51Z6giFaxIoBSkzuCSTzXYM2J/aiHfgKDigA0WmPsWsmRCmnwhYCcchSYnRb0IbWi5NqztfjUyCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"338d97dabfeebfc1e20391a36f75f6ac7740c5d8dd82155889e6f9a5a8a485ae","last_reissued_at":"2026-05-17T23:49:17.647876Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:17.647876Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Rate-Optimal Denoising with Deep Neural Networks","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.SP","math.IT","math.OC"],"primary_cat":"cs.IT","authors_text":"Paul Hand, Reinhard Heckel, Vladislav Voroninski, Wen Huang","submitted_at":"2018-05-22T20:33:52Z","abstract_excerpt":"Deep neural networks provide state-of-the-art performance for image denoising, where the goal is to recover a near noise-free image from a noisy observation. The underlying principle is that neural networks trained on large datasets have empirically been shown to be able to generate natural images well from a low-dimensional latent representation of the image. Given such a generator network, a noisy image can be denoised by i) finding the closest image in the range of the generator or by ii) passing it through an encoder-generator architecture (known as an autoencoder). However, there is littl"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1805.08855","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":"1805.08855","created_at":"2026-05-17T23:49:17.647976+00:00"},{"alias_kind":"arxiv_version","alias_value":"1805.08855v2","created_at":"2026-05-17T23:49:17.647976+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1805.08855","created_at":"2026-05-17T23:49:17.647976+00:00"},{"alias_kind":"pith_short_12","alias_value":"GOGZPWV75274","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_16","alias_value":"GOGZPWV75274DYQD","created_at":"2026-05-18T12:32:25.280505+00:00"},{"alias_kind":"pith_short_8","alias_value":"GOGZPWV7","created_at":"2026-05-18T12:32:25.280505+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/GOGZPWV75274DYQDSGRW65PWVR","json":"https://pith.science/pith/GOGZPWV75274DYQDSGRW65PWVR.json","graph_json":"https://pith.science/api/pith-number/GOGZPWV75274DYQDSGRW65PWVR/graph.json","events_json":"https://pith.science/api/pith-number/GOGZPWV75274DYQDSGRW65PWVR/events.json","paper":"https://pith.science/paper/GOGZPWV7"},"agent_actions":{"view_html":"https://pith.science/pith/GOGZPWV75274DYQDSGRW65PWVR","download_json":"https://pith.science/pith/GOGZPWV75274DYQDSGRW65PWVR.json","view_paper":"https://pith.science/paper/GOGZPWV7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1805.08855&json=true","fetch_graph":"https://pith.science/api/pith-number/GOGZPWV75274DYQDSGRW65PWVR/graph.json","fetch_events":"https://pith.science/api/pith-number/GOGZPWV75274DYQDSGRW65PWVR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GOGZPWV75274DYQDSGRW65PWVR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GOGZPWV75274DYQDSGRW65PWVR/action/storage_attestation","attest_author":"https://pith.science/pith/GOGZPWV75274DYQDSGRW65PWVR/action/author_attestation","sign_citation":"https://pith.science/pith/GOGZPWV75274DYQDSGRW65PWVR/action/citation_signature","submit_replication":"https://pith.science/pith/GOGZPWV75274DYQDSGRW65PWVR/action/replication_record"}},"created_at":"2026-05-17T23:49:17.647976+00:00","updated_at":"2026-05-17T23:49:17.647976+00:00"}