{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:HE4XPF736E7HY4V2KU2QAEFEOD","short_pith_number":"pith:HE4XPF73","schema_version":"1.0","canonical_sha256":"39397797fbf13e7c72ba55350010a470ceec3f92715d88613e684d6b41babcca","source":{"kind":"arxiv","id":"1802.08406","version":1},"attestation_state":"computed","paper":{"title":"Solving Linear Inverse Problems Using GAN Priors: An Algorithm with Provable Guarantees","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Chinmay Hegde, Viraj Shah","submitted_at":"2018-02-23T06:40:58Z","abstract_excerpt":"In recent works, both sparsity-based methods as well as learning-based methods have proven to be successful in solving several challenging linear inverse problems. However, sparsity priors for natural signals and images suffer from poor discriminative capability, while learning-based methods seldom provide concrete theoretical guarantees. In this work, we advocate the idea of replacing hand-crafted priors, such as sparsity, with a Generative Adversarial Network (GAN) to solve linear inverse problems such as compressive sensing. In particular, we propose a projected gradient descent (PGD) algor"},"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":"1802.08406","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-02-23T06:40:58Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c484ff1fffaf01c5d63fbb052f059df72d1001139ce0d6beaef42c2939edb5d6","abstract_canon_sha256":"5f435b08df0617d4002782c60f77ba893c77e0d2ea3de78460f9ba5637c797cc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:22:43.809348Z","signature_b64":"vZaCXFWfAmOEsLhLHngfQpjSaUXIXEYjlvAeap/DomKKorBcxWO7UKGKvjFyO+ttjTiwzERLoUyHYQ/NHghBAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"39397797fbf13e7c72ba55350010a470ceec3f92715d88613e684d6b41babcca","last_reissued_at":"2026-05-18T00:22:43.808880Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:22:43.808880Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Solving Linear Inverse Problems Using GAN Priors: An Algorithm with Provable Guarantees","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Chinmay Hegde, Viraj Shah","submitted_at":"2018-02-23T06:40:58Z","abstract_excerpt":"In recent works, both sparsity-based methods as well as learning-based methods have proven to be successful in solving several challenging linear inverse problems. However, sparsity priors for natural signals and images suffer from poor discriminative capability, while learning-based methods seldom provide concrete theoretical guarantees. In this work, we advocate the idea of replacing hand-crafted priors, such as sparsity, with a Generative Adversarial Network (GAN) to solve linear inverse problems such as compressive sensing. In particular, we propose a projected gradient descent (PGD) algor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.08406","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":"1802.08406","created_at":"2026-05-18T00:22:43.808948+00:00"},{"alias_kind":"arxiv_version","alias_value":"1802.08406v1","created_at":"2026-05-18T00:22:43.808948+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.08406","created_at":"2026-05-18T00:22:43.808948+00:00"},{"alias_kind":"pith_short_12","alias_value":"HE4XPF736E7H","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_16","alias_value":"HE4XPF736E7HY4V2","created_at":"2026-05-18T12:32:28.185984+00:00"},{"alias_kind":"pith_short_8","alias_value":"HE4XPF73","created_at":"2026-05-18T12:32:28.185984+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/HE4XPF736E7HY4V2KU2QAEFEOD","json":"https://pith.science/pith/HE4XPF736E7HY4V2KU2QAEFEOD.json","graph_json":"https://pith.science/api/pith-number/HE4XPF736E7HY4V2KU2QAEFEOD/graph.json","events_json":"https://pith.science/api/pith-number/HE4XPF736E7HY4V2KU2QAEFEOD/events.json","paper":"https://pith.science/paper/HE4XPF73"},"agent_actions":{"view_html":"https://pith.science/pith/HE4XPF736E7HY4V2KU2QAEFEOD","download_json":"https://pith.science/pith/HE4XPF736E7HY4V2KU2QAEFEOD.json","view_paper":"https://pith.science/paper/HE4XPF73","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1802.08406&json=true","fetch_graph":"https://pith.science/api/pith-number/HE4XPF736E7HY4V2KU2QAEFEOD/graph.json","fetch_events":"https://pith.science/api/pith-number/HE4XPF736E7HY4V2KU2QAEFEOD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HE4XPF736E7HY4V2KU2QAEFEOD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HE4XPF736E7HY4V2KU2QAEFEOD/action/storage_attestation","attest_author":"https://pith.science/pith/HE4XPF736E7HY4V2KU2QAEFEOD/action/author_attestation","sign_citation":"https://pith.science/pith/HE4XPF736E7HY4V2KU2QAEFEOD/action/citation_signature","submit_replication":"https://pith.science/pith/HE4XPF736E7HY4V2KU2QAEFEOD/action/replication_record"}},"created_at":"2026-05-18T00:22:43.808948+00:00","updated_at":"2026-05-18T00:22:43.808948+00:00"}