{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:5BNY6OIZ22FOJOU23IW2IWOFYI","short_pith_number":"pith:5BNY6OIZ","canonical_record":{"source":{"id":"2007.10538","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-07-21T00:32:44Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"144277241fd055b05386afc954298cf4f99c76b4a082139343fe40d9a12b81c7","abstract_canon_sha256":"e6697064012d4d842af7f54bae880a9f464f0307d22b8c0dc6ec7464c0a92fdf"},"schema_version":"1.0"},"canonical_sha256":"e85b8f3919d68ae4ba9ada2da459c5c206eb04d473d8036ba50284e2f32ed3df","source":{"kind":"arxiv","id":"2007.10538","version":5},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2007.10538","created_at":"2026-07-05T02:46:09Z"},{"alias_kind":"arxiv_version","alias_value":"2007.10538v5","created_at":"2026-07-05T02:46:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.10538","created_at":"2026-07-05T02:46:09Z"},{"alias_kind":"pith_short_12","alias_value":"5BNY6OIZ22FO","created_at":"2026-07-05T02:46:09Z"},{"alias_kind":"pith_short_16","alias_value":"5BNY6OIZ22FOJOU2","created_at":"2026-07-05T02:46:09Z"},{"alias_kind":"pith_short_8","alias_value":"5BNY6OIZ","created_at":"2026-07-05T02:46:09Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:5BNY6OIZ22FOJOU23IW2IWOFYI","target":"record","payload":{"canonical_record":{"source":{"id":"2007.10538","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-07-21T00:32:44Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"144277241fd055b05386afc954298cf4f99c76b4a082139343fe40d9a12b81c7","abstract_canon_sha256":"e6697064012d4d842af7f54bae880a9f464f0307d22b8c0dc6ec7464c0a92fdf"},"schema_version":"1.0"},"canonical_sha256":"e85b8f3919d68ae4ba9ada2da459c5c206eb04d473d8036ba50284e2f32ed3df","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:46:09.596413Z","signature_b64":"GmLTUs88Vzzar7ftG+5Qetji1/3UAZuP4t6qmb1DcFiY/aOdZmkcZVpyXSL958b2A05BsKogZrIeEiidSgosDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e85b8f3919d68ae4ba9ada2da459c5c206eb04d473d8036ba50284e2f32ed3df","last_reissued_at":"2026-07-05T02:46:09.595879Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:46:09.595879Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2007.10538","source_version":5,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T02:46:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"g7OC1sNM6qZedeIV9nfWWlJBPN0pmXG0p9SuDKVTmWH92IfSblwv27D3qA9nuzjvWxijlmrnePqawJZ28rxBCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T10:49:41.840652Z"},"content_sha256":"4e619345ad9fd4a1f301805657d325921d53ff79f115f6e806de95cb903d5e5a","schema_version":"1.0","event_id":"sha256:4e619345ad9fd4a1f301805657d325921d53ff79f115f6e806de95cb903d5e5a"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:5BNY6OIZ22FOJOU23IW2IWOFYI","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Regularizing Deep Networks with Semantic Data Augmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Cheng Wu, Gao Huang, Shiji Song, Xuran Pan, Yitong Xia, Yulin Wang","submitted_at":"2020-07-21T00:32:44Z","abstract_excerpt":"Data augmentation is widely known as a simple yet surprisingly effective technique for regularizing deep networks. Conventional data augmentation schemes, e.g., flipping, translation or rotation, are low-level, data-independent and class-agnostic operations, leading to limited diversity for augmented samples. To this end, we propose a novel semantic data augmentation algorithm to complement traditional approaches. The proposed method is inspired by the intriguing property that deep networks are effective in learning linearized features, i.e., certain directions in the deep feature space corres"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.10538","kind":"arxiv","version":5},"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/2007.10538/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":"2026-07-05T02:46:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OUBiJGJc1kCoUZBEV5eY3fzrd5s6awRRC56GvExGHDMageRBjSz9iiAjVX22xmCTTAfGvBJnPGce8f0jDCUJAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T10:49:41.841026Z"},"content_sha256":"0c5b549a1dc83fa94ed331881331f40179acfe6c1e894078c1c70deb42db7a57","schema_version":"1.0","event_id":"sha256:0c5b549a1dc83fa94ed331881331f40179acfe6c1e894078c1c70deb42db7a57"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5BNY6OIZ22FOJOU23IW2IWOFYI/bundle.json","state_url":"https://pith.science/pith/5BNY6OIZ22FOJOU23IW2IWOFYI/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5BNY6OIZ22FOJOU23IW2IWOFYI/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":"2026-07-06T10:49:41Z","links":{"resolver":"https://pith.science/pith/5BNY6OIZ22FOJOU23IW2IWOFYI","bundle":"https://pith.science/pith/5BNY6OIZ22FOJOU23IW2IWOFYI/bundle.json","state":"https://pith.science/pith/5BNY6OIZ22FOJOU23IW2IWOFYI/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5BNY6OIZ22FOJOU23IW2IWOFYI/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:5BNY6OIZ22FOJOU23IW2IWOFYI","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":"e6697064012d4d842af7f54bae880a9f464f0307d22b8c0dc6ec7464c0a92fdf","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-07-21T00:32:44Z","title_canon_sha256":"144277241fd055b05386afc954298cf4f99c76b4a082139343fe40d9a12b81c7"},"schema_version":"1.0","source":{"id":"2007.10538","kind":"arxiv","version":5}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2007.10538","created_at":"2026-07-05T02:46:09Z"},{"alias_kind":"arxiv_version","alias_value":"2007.10538v5","created_at":"2026-07-05T02:46:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.10538","created_at":"2026-07-05T02:46:09Z"},{"alias_kind":"pith_short_12","alias_value":"5BNY6OIZ22FO","created_at":"2026-07-05T02:46:09Z"},{"alias_kind":"pith_short_16","alias_value":"5BNY6OIZ22FOJOU2","created_at":"2026-07-05T02:46:09Z"},{"alias_kind":"pith_short_8","alias_value":"5BNY6OIZ","created_at":"2026-07-05T02:46:09Z"}],"graph_snapshots":[{"event_id":"sha256:0c5b549a1dc83fa94ed331881331f40179acfe6c1e894078c1c70deb42db7a57","target":"graph","created_at":"2026-07-05T02:46:09Z","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/2007.10538/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Data augmentation is widely known as a simple yet surprisingly effective technique for regularizing deep networks. Conventional data augmentation schemes, e.g., flipping, translation or rotation, are low-level, data-independent and class-agnostic operations, leading to limited diversity for augmented samples. To this end, we propose a novel semantic data augmentation algorithm to complement traditional approaches. The proposed method is inspired by the intriguing property that deep networks are effective in learning linearized features, i.e., certain directions in the deep feature space corres","authors_text":"Cheng Wu, Gao Huang, Shiji Song, Xuran Pan, Yitong Xia, Yulin Wang","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-07-21T00:32:44Z","title":"Regularizing Deep Networks with Semantic Data Augmentation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.10538","kind":"arxiv","version":5},"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":"sha256:4e619345ad9fd4a1f301805657d325921d53ff79f115f6e806de95cb903d5e5a","target":"record","created_at":"2026-07-05T02:46:09Z","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":"e6697064012d4d842af7f54bae880a9f464f0307d22b8c0dc6ec7464c0a92fdf","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-07-21T00:32:44Z","title_canon_sha256":"144277241fd055b05386afc954298cf4f99c76b4a082139343fe40d9a12b81c7"},"schema_version":"1.0","source":{"id":"2007.10538","kind":"arxiv","version":5}},"canonical_sha256":"e85b8f3919d68ae4ba9ada2da459c5c206eb04d473d8036ba50284e2f32ed3df","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e85b8f3919d68ae4ba9ada2da459c5c206eb04d473d8036ba50284e2f32ed3df","first_computed_at":"2026-07-05T02:46:09.595879Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:46:09.595879Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"GmLTUs88Vzzar7ftG+5Qetji1/3UAZuP4t6qmb1DcFiY/aOdZmkcZVpyXSL958b2A05BsKogZrIeEiidSgosDQ==","signature_status":"signed_v1","signed_at":"2026-07-05T02:46:09.596413Z","signed_message":"canonical_sha256_bytes"},"source_id":"2007.10538","source_kind":"arxiv","source_version":5}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4e619345ad9fd4a1f301805657d325921d53ff79f115f6e806de95cb903d5e5a","sha256:0c5b549a1dc83fa94ed331881331f40179acfe6c1e894078c1c70deb42db7a57"],"state_sha256":"396534310e89f9f1746bb01c995c4305fc4683b022ad265f0bec546149fbdc34"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"VJvey3s8yYBt4JWvWtNaKt+u/no4su2XL/j9h7RIjX+WTar4BRkplEEKdxoKySIojqPaGq23xZvOf6VFegP7AA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T10:49:41.842997Z","bundle_sha256":"4f0ad9613ab6a4b9a452e6d6db5dfebecbf6e3a9132988f7db6928fb79c1e1d0"}}