{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:LSBHLXYJF2LI4SSCQ5NA3WQIYX","short_pith_number":"pith:LSBHLXYJ","schema_version":"1.0","canonical_sha256":"5c8275df092e968e4a42875a0dda08c5c201808ea0ad7d12029058439a7b9159","source":{"kind":"arxiv","id":"1810.10108","version":1},"attestation_state":"computed","paper":{"title":"Reproducing AmbientGAN: Generative models from lossy measurements","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"eess.SP","authors_text":"Mehdi Ahmadi, Mostafa Abdelnaim, Thanh-Dung Le, Timothy Nest","submitted_at":"2018-10-23T22:10:51Z","abstract_excerpt":"In recent years, Generative Adversarial Networks (GANs) have shown substantial progress in modeling complex distributions of data. These networks have received tremendous attention since they can generate implicit probabilistic models that produce realistic data using a stochastic procedure. While such models have proven highly effective in diverse scenarios, they require a large set of fully-observed training samples. In many applications access to such samples are difficult or even impractical and only noisy or partial observations of the desired distribution is available. Recent research ha"},"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":"1810.10108","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.SP","submitted_at":"2018-10-23T22:10:51Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"f2f8c9a8d0a595aae952a232b886a63ecdd7347fc0159f726cf4c3f24374161e","abstract_canon_sha256":"9c95db039a51b3edd8bc99274a8da30bf9c3e980b1a85fb09af40160bd0cfb8f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:02:24.221791Z","signature_b64":"gDpmk4tzJgX8CNPf8z54qZxVHQvo0CujGHN+fmtHl/kxMiepmMMWDVyVc7rdxbwPVMPubUNiY1/+ZOap4G8CDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5c8275df092e968e4a42875a0dda08c5c201808ea0ad7d12029058439a7b9159","last_reissued_at":"2026-05-18T00:02:24.221233Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:02:24.221233Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Reproducing AmbientGAN: Generative models from lossy measurements","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"eess.SP","authors_text":"Mehdi Ahmadi, Mostafa Abdelnaim, Thanh-Dung Le, Timothy Nest","submitted_at":"2018-10-23T22:10:51Z","abstract_excerpt":"In recent years, Generative Adversarial Networks (GANs) have shown substantial progress in modeling complex distributions of data. These networks have received tremendous attention since they can generate implicit probabilistic models that produce realistic data using a stochastic procedure. While such models have proven highly effective in diverse scenarios, they require a large set of fully-observed training samples. In many applications access to such samples are difficult or even impractical and only noisy or partial observations of the desired distribution is available. Recent research ha"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.10108","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":"1810.10108","created_at":"2026-05-18T00:02:24.221341+00:00"},{"alias_kind":"arxiv_version","alias_value":"1810.10108v1","created_at":"2026-05-18T00:02:24.221341+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.10108","created_at":"2026-05-18T00:02:24.221341+00:00"},{"alias_kind":"pith_short_12","alias_value":"LSBHLXYJF2LI","created_at":"2026-05-18T12:32:37.024351+00:00"},{"alias_kind":"pith_short_16","alias_value":"LSBHLXYJF2LI4SSC","created_at":"2026-05-18T12:32:37.024351+00:00"},{"alias_kind":"pith_short_8","alias_value":"LSBHLXYJ","created_at":"2026-05-18T12:32:37.024351+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/LSBHLXYJF2LI4SSCQ5NA3WQIYX","json":"https://pith.science/pith/LSBHLXYJF2LI4SSCQ5NA3WQIYX.json","graph_json":"https://pith.science/api/pith-number/LSBHLXYJF2LI4SSCQ5NA3WQIYX/graph.json","events_json":"https://pith.science/api/pith-number/LSBHLXYJF2LI4SSCQ5NA3WQIYX/events.json","paper":"https://pith.science/paper/LSBHLXYJ"},"agent_actions":{"view_html":"https://pith.science/pith/LSBHLXYJF2LI4SSCQ5NA3WQIYX","download_json":"https://pith.science/pith/LSBHLXYJF2LI4SSCQ5NA3WQIYX.json","view_paper":"https://pith.science/paper/LSBHLXYJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1810.10108&json=true","fetch_graph":"https://pith.science/api/pith-number/LSBHLXYJF2LI4SSCQ5NA3WQIYX/graph.json","fetch_events":"https://pith.science/api/pith-number/LSBHLXYJF2LI4SSCQ5NA3WQIYX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LSBHLXYJF2LI4SSCQ5NA3WQIYX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LSBHLXYJF2LI4SSCQ5NA3WQIYX/action/storage_attestation","attest_author":"https://pith.science/pith/LSBHLXYJF2LI4SSCQ5NA3WQIYX/action/author_attestation","sign_citation":"https://pith.science/pith/LSBHLXYJF2LI4SSCQ5NA3WQIYX/action/citation_signature","submit_replication":"https://pith.science/pith/LSBHLXYJF2LI4SSCQ5NA3WQIYX/action/replication_record"}},"created_at":"2026-05-18T00:02:24.221341+00:00","updated_at":"2026-05-18T00:02:24.221341+00:00"}