{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:G55JJT7W3B6HFRRLSJNYTV43ZE","short_pith_number":"pith:G55JJT7W","canonical_record":{"source":{"id":"2103.16493","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2021-03-30T16:49:20Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"1d4d177e1c6b22f29433a2775ab20897c62878052c559a60a76fd2bc56748a59","abstract_canon_sha256":"2f4afe496ae1380d0b8449c3927efcf18df346ae1d9bf90144b45783c76388b3"},"schema_version":"1.0"},"canonical_sha256":"377a94cff6d87c72c62b925b89d79bc90e878b9c51c8a63ca3c27fc8670bebcd","source":{"kind":"arxiv","id":"2103.16493","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.16493","created_at":"2026-07-05T02:27:52Z"},{"alias_kind":"arxiv_version","alias_value":"2103.16493v1","created_at":"2026-07-05T02:27:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.16493","created_at":"2026-07-05T02:27:52Z"},{"alias_kind":"pith_short_12","alias_value":"G55JJT7W3B6H","created_at":"2026-07-05T02:27:52Z"},{"alias_kind":"pith_short_16","alias_value":"G55JJT7W3B6HFRRL","created_at":"2026-07-05T02:27:52Z"},{"alias_kind":"pith_short_8","alias_value":"G55JJT7W","created_at":"2026-07-05T02:27:52Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:G55JJT7W3B6HFRRLSJNYTV43ZE","target":"record","payload":{"canonical_record":{"source":{"id":"2103.16493","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2021-03-30T16:49:20Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"1d4d177e1c6b22f29433a2775ab20897c62878052c559a60a76fd2bc56748a59","abstract_canon_sha256":"2f4afe496ae1380d0b8449c3927efcf18df346ae1d9bf90144b45783c76388b3"},"schema_version":"1.0"},"canonical_sha256":"377a94cff6d87c72c62b925b89d79bc90e878b9c51c8a63ca3c27fc8670bebcd","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:27:52.660062Z","signature_b64":"pFhcQNBVC2cWodQ4OevwiV4JBfnXt0qfvMKylPYtiA/gi5oVW5l6P/kSDHxjUZRi8APDHlcxmGJh6TXB2G3ABw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"377a94cff6d87c72c62b925b89d79bc90e878b9c51c8a63ca3c27fc8670bebcd","last_reissued_at":"2026-07-05T02:27:52.659649Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:27:52.659649Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2103.16493","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:27:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/l3zXGHJrv8cEHDp0EghzdZr5QSJka0Ho95vEzm2DqOGK/4nl8yv8psC3sSAytf8kRyro3k/BsMaF38guxIGCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T19:38:53.967702Z"},"content_sha256":"49bab0db29bd49b1adb162baf2a037c4b9b71ae2df0ba069cf6073f260bb4165","schema_version":"1.0","event_id":"sha256:49bab0db29bd49b1adb162baf2a037c4b9b71ae2df0ba069cf6073f260bb4165"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:G55JJT7W3B6HFRRLSJNYTV43ZE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Enabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial Training","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Dimitris Metaxas, Mu Zhou, Yunhe Gao, Zhiqiang Tang","submitted_at":"2021-03-30T16:49:20Z","abstract_excerpt":"Data augmentation has proved extremely useful by increasing training data variance to alleviate overfitting and improve deep neural networks' generalization performance. In medical image analysis, a well-designed augmentation policy usually requires much expert knowledge and is difficult to generalize to multiple tasks due to the vast discrepancies among pixel intensities, image appearances, and object shapes in different medical tasks. To automate medical data augmentation, we propose a regularized adversarial training framework via two min-max objectives and three differentiable augmentation"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.16493","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2103.16493/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":"2026-07-05T02:27:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XWsrC0CjBp3zwobIEuUZVoSX68KqrB5QgVfwhy6apIy2FLc+EH7G96yQJcQ1L4py4Fh+Oh1Ht0lafeCF3Um5DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T19:38:53.968256Z"},"content_sha256":"73a786ad8974c66479c445389b295320d6a90edc7a3f96bc0da1fe6fe22bace5","schema_version":"1.0","event_id":"sha256:73a786ad8974c66479c445389b295320d6a90edc7a3f96bc0da1fe6fe22bace5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/G55JJT7W3B6HFRRLSJNYTV43ZE/bundle.json","state_url":"https://pith.science/pith/G55JJT7W3B6HFRRLSJNYTV43ZE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/G55JJT7W3B6HFRRLSJNYTV43ZE/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":"2026-08-18T19:38:53Z","links":{"resolver":"https://pith.science/pith/G55JJT7W3B6HFRRLSJNYTV43ZE","bundle":"https://pith.science/pith/G55JJT7W3B6HFRRLSJNYTV43ZE/bundle.json","state":"https://pith.science/pith/G55JJT7W3B6HFRRLSJNYTV43ZE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/G55JJT7W3B6HFRRLSJNYTV43ZE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:G55JJT7W3B6HFRRLSJNYTV43ZE","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":"2f4afe496ae1380d0b8449c3927efcf18df346ae1d9bf90144b45783c76388b3","cross_cats_sorted":["eess.IV"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2021-03-30T16:49:20Z","title_canon_sha256":"1d4d177e1c6b22f29433a2775ab20897c62878052c559a60a76fd2bc56748a59"},"schema_version":"1.0","source":{"id":"2103.16493","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2103.16493","created_at":"2026-07-05T02:27:52Z"},{"alias_kind":"arxiv_version","alias_value":"2103.16493v1","created_at":"2026-07-05T02:27:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.16493","created_at":"2026-07-05T02:27:52Z"},{"alias_kind":"pith_short_12","alias_value":"G55JJT7W3B6H","created_at":"2026-07-05T02:27:52Z"},{"alias_kind":"pith_short_16","alias_value":"G55JJT7W3B6HFRRL","created_at":"2026-07-05T02:27:52Z"},{"alias_kind":"pith_short_8","alias_value":"G55JJT7W","created_at":"2026-07-05T02:27:52Z"}],"graph_snapshots":[{"event_id":"sha256:73a786ad8974c66479c445389b295320d6a90edc7a3f96bc0da1fe6fe22bace5","target":"graph","created_at":"2026-07-05T02:27:52Z","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/2103.16493/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Data augmentation has proved extremely useful by increasing training data variance to alleviate overfitting and improve deep neural networks' generalization performance. In medical image analysis, a well-designed augmentation policy usually requires much expert knowledge and is difficult to generalize to multiple tasks due to the vast discrepancies among pixel intensities, image appearances, and object shapes in different medical tasks. To automate medical data augmentation, we propose a regularized adversarial training framework via two min-max objectives and three differentiable augmentation","authors_text":"Dimitris Metaxas, Mu Zhou, Yunhe Gao, Zhiqiang Tang","cross_cats":["eess.IV"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2021-03-30T16:49:20Z","title":"Enabling Data Diversity: Efficient Automatic Augmentation via Regularized Adversarial Training"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.16493","kind":"arxiv","version":1},"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":"sha256:49bab0db29bd49b1adb162baf2a037c4b9b71ae2df0ba069cf6073f260bb4165","target":"record","created_at":"2026-07-05T02:27:52Z","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":"2f4afe496ae1380d0b8449c3927efcf18df346ae1d9bf90144b45783c76388b3","cross_cats_sorted":["eess.IV"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2021-03-30T16:49:20Z","title_canon_sha256":"1d4d177e1c6b22f29433a2775ab20897c62878052c559a60a76fd2bc56748a59"},"schema_version":"1.0","source":{"id":"2103.16493","kind":"arxiv","version":1}},"canonical_sha256":"377a94cff6d87c72c62b925b89d79bc90e878b9c51c8a63ca3c27fc8670bebcd","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"377a94cff6d87c72c62b925b89d79bc90e878b9c51c8a63ca3c27fc8670bebcd","first_computed_at":"2026-07-05T02:27:52.659649Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:27:52.659649Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"pFhcQNBVC2cWodQ4OevwiV4JBfnXt0qfvMKylPYtiA/gi5oVW5l6P/kSDHxjUZRi8APDHlcxmGJh6TXB2G3ABw==","signature_status":"signed_v1","signed_at":"2026-07-05T02:27:52.660062Z","signed_message":"canonical_sha256_bytes"},"source_id":"2103.16493","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:49bab0db29bd49b1adb162baf2a037c4b9b71ae2df0ba069cf6073f260bb4165","sha256:73a786ad8974c66479c445389b295320d6a90edc7a3f96bc0da1fe6fe22bace5"],"state_sha256":"c399333d1b8e8b0d1229b039a9cbf8364b27a1fa7ed476eb01404768cc9a33a2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bcGXvHF4M0D3ukHZPvHzvt11DiayxkCc4u8l1+RpmI0dNOHPJhcQr9mxw+qegWUI6skpuAYFQjXDYDApTiHECg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T19:38:53.975241Z","bundle_sha256":"4b64e21ae2265b069152041473275678661b3ca3d6bd7f56afef781ab699d184"}}