{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:EQ7XRIAHUEN46ISKQCPE42X3A3","short_pith_number":"pith:EQ7XRIAH","canonical_record":{"source":{"id":"1910.01634","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-10-03T17:52:14Z","cross_cats_sorted":["cs.CV","cs.LG","stat.ML"],"title_canon_sha256":"3fbf0cfbb538ba09acfcb46a92df73748be1c574409a43ebdb32cd4e4f04ba35","abstract_canon_sha256":"e3bc75f69d72a67317b56eb2b35a943fa4273ab6253ab346787e50037f91c0a2"},"schema_version":"1.0"},"canonical_sha256":"243f78a007a11bcf224a809e4e6afb06c10cdf56aa1e196c585a053afcd46444","source":{"kind":"arxiv","id":"1910.01634","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.01634","created_at":"2026-07-05T00:37:08Z"},{"alias_kind":"arxiv_version","alias_value":"1910.01634v4","created_at":"2026-07-05T00:37:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.01634","created_at":"2026-07-05T00:37:08Z"},{"alias_kind":"pith_short_12","alias_value":"EQ7XRIAHUEN4","created_at":"2026-07-05T00:37:08Z"},{"alias_kind":"pith_short_16","alias_value":"EQ7XRIAHUEN46ISK","created_at":"2026-07-05T00:37:08Z"},{"alias_kind":"pith_short_8","alias_value":"EQ7XRIAH","created_at":"2026-07-05T00:37:08Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:EQ7XRIAHUEN46ISKQCPE42X3A3","target":"record","payload":{"canonical_record":{"source":{"id":"1910.01634","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-10-03T17:52:14Z","cross_cats_sorted":["cs.CV","cs.LG","stat.ML"],"title_canon_sha256":"3fbf0cfbb538ba09acfcb46a92df73748be1c574409a43ebdb32cd4e4f04ba35","abstract_canon_sha256":"e3bc75f69d72a67317b56eb2b35a943fa4273ab6253ab346787e50037f91c0a2"},"schema_version":"1.0"},"canonical_sha256":"243f78a007a11bcf224a809e4e6afb06c10cdf56aa1e196c585a053afcd46444","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:37:08.323648Z","signature_b64":"KfAacRDoqWuPYMmwn7VuylvtVIAu3w8k8/TVnZHadnMc4bFB4pHIAyoRByKktytQOAHkNxhKw+RaWATNrsuLAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"243f78a007a11bcf224a809e4e6afb06c10cdf56aa1e196c585a053afcd46444","last_reissued_at":"2026-07-05T00:37:08.323141Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:37:08.323141Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1910.01634","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:37:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VTEgL2XLCs2iHuj6oATm9am6JDkMMsWRkoVqCMND9nniD5T5zvnCTHy0wHS8ZYsJLZfeU0LGSaSnS7lK1+u0Dg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T18:18:36.161344Z"},"content_sha256":"b24225927827019cecad26d9f8b159044d718d7aed1c162cd0092be1c6c70557","schema_version":"1.0","event_id":"sha256:b24225927827019cecad26d9f8b159044d718d7aed1c162cd0092be1c6c70557"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:EQ7XRIAHUEN46ISKQCPE42X3A3","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Improving Limited Angle CT Reconstruction with a Robust GAN Prior","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG","stat.ML"],"primary_cat":"eess.IV","authors_text":"Hyojin Kim, Jayaraman J. Thiagarajan, K. Aditya Mohan, Kyle M. Champley, Rushil Anirudh","submitted_at":"2019-10-03T17:52:14Z","abstract_excerpt":"Limited angle CT reconstruction is an under-determined linear inverse problem that requires appropriate regularization techniques to be solved. In this work we study how pre-trained generative adversarial networks (GANs) can be used to clean noisy, highly artifact laden reconstructions from conventional techniques, by effectively projecting onto the inferred image manifold. In particular, we use a robust version of the popularly used GAN prior for inverse problems, based on a recent technique called corruption mimicking, that significantly improves the reconstruction quality. The proposed appr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.01634","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1910.01634/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:37:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"k5yhvjcb5mVc6DCb5TesH/zMiIw2SGirp3XOLFnmmuEO2xG4n+QwaZIAQz3RvCvE6xOD3GIehwx7JJTSMMdDAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T18:18:36.161893Z"},"content_sha256":"c814d5a562c88477c038ba68015497277be483b2cf180e935544073fae9989eb","schema_version":"1.0","event_id":"sha256:c814d5a562c88477c038ba68015497277be483b2cf180e935544073fae9989eb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/EQ7XRIAHUEN46ISKQCPE42X3A3/bundle.json","state_url":"https://pith.science/pith/EQ7XRIAHUEN46ISKQCPE42X3A3/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/EQ7XRIAHUEN46ISKQCPE42X3A3/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-16T18:18:36Z","links":{"resolver":"https://pith.science/pith/EQ7XRIAHUEN46ISKQCPE42X3A3","bundle":"https://pith.science/pith/EQ7XRIAHUEN46ISKQCPE42X3A3/bundle.json","state":"https://pith.science/pith/EQ7XRIAHUEN46ISKQCPE42X3A3/state.json","well_known_bundle":"https://pith.science/.well-known/pith/EQ7XRIAHUEN46ISKQCPE42X3A3/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:EQ7XRIAHUEN46ISKQCPE42X3A3","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"e3bc75f69d72a67317b56eb2b35a943fa4273ab6253ab346787e50037f91c0a2","cross_cats_sorted":["cs.CV","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-10-03T17:52:14Z","title_canon_sha256":"3fbf0cfbb538ba09acfcb46a92df73748be1c574409a43ebdb32cd4e4f04ba35"},"schema_version":"1.0","source":{"id":"1910.01634","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.01634","created_at":"2026-07-05T00:37:08Z"},{"alias_kind":"arxiv_version","alias_value":"1910.01634v4","created_at":"2026-07-05T00:37:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.01634","created_at":"2026-07-05T00:37:08Z"},{"alias_kind":"pith_short_12","alias_value":"EQ7XRIAHUEN4","created_at":"2026-07-05T00:37:08Z"},{"alias_kind":"pith_short_16","alias_value":"EQ7XRIAHUEN46ISK","created_at":"2026-07-05T00:37:08Z"},{"alias_kind":"pith_short_8","alias_value":"EQ7XRIAH","created_at":"2026-07-05T00:37:08Z"}],"graph_snapshots":[{"event_id":"sha256:c814d5a562c88477c038ba68015497277be483b2cf180e935544073fae9989eb","target":"graph","created_at":"2026-07-05T00:37:08Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1910.01634/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Limited angle CT reconstruction is an under-determined linear inverse problem that requires appropriate regularization techniques to be solved. In this work we study how pre-trained generative adversarial networks (GANs) can be used to clean noisy, highly artifact laden reconstructions from conventional techniques, by effectively projecting onto the inferred image manifold. In particular, we use a robust version of the popularly used GAN prior for inverse problems, based on a recent technique called corruption mimicking, that significantly improves the reconstruction quality. The proposed appr","authors_text":"Hyojin Kim, Jayaraman J. Thiagarajan, K. Aditya Mohan, Kyle M. Champley, Rushil Anirudh","cross_cats":["cs.CV","cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-10-03T17:52:14Z","title":"Improving Limited Angle CT Reconstruction with a Robust GAN Prior"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.01634","kind":"arxiv","version":4},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:b24225927827019cecad26d9f8b159044d718d7aed1c162cd0092be1c6c70557","target":"record","created_at":"2026-07-05T00:37:08Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"e3bc75f69d72a67317b56eb2b35a943fa4273ab6253ab346787e50037f91c0a2","cross_cats_sorted":["cs.CV","cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-10-03T17:52:14Z","title_canon_sha256":"3fbf0cfbb538ba09acfcb46a92df73748be1c574409a43ebdb32cd4e4f04ba35"},"schema_version":"1.0","source":{"id":"1910.01634","kind":"arxiv","version":4}},"canonical_sha256":"243f78a007a11bcf224a809e4e6afb06c10cdf56aa1e196c585a053afcd46444","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"243f78a007a11bcf224a809e4e6afb06c10cdf56aa1e196c585a053afcd46444","first_computed_at":"2026-07-05T00:37:08.323141Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:37:08.323141Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"KfAacRDoqWuPYMmwn7VuylvtVIAu3w8k8/TVnZHadnMc4bFB4pHIAyoRByKktytQOAHkNxhKw+RaWATNrsuLAA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:37:08.323648Z","signed_message":"canonical_sha256_bytes"},"source_id":"1910.01634","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b24225927827019cecad26d9f8b159044d718d7aed1c162cd0092be1c6c70557","sha256:c814d5a562c88477c038ba68015497277be483b2cf180e935544073fae9989eb"],"state_sha256":"f65ade960ef483df4e9f04efdedccb4156ae60f0d2f01ec2fa5064fa71ab6c2c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8NHMzzM0QFNgE9MpaJdLiJyXI+bNwlwQ6ZtytUStjbs5w1ELJwqREu436tYtQV7sZco0nYY7KkgyIOEwXuINBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T18:18:36.167059Z","bundle_sha256":"239a7c6cb4a621a1dbb6bfc7e62f626a6d0afb9f8366608a655a7d47b0d34ab9"}}