{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:N2Y7FUWCAQL3OAPK5DRWTFG7KM","short_pith_number":"pith:N2Y7FUWC","schema_version":"1.0","canonical_sha256":"6eb1f2d2c20417b701eae8e36994df53332cd8e79a9811293e548e9d60641836","source":{"kind":"arxiv","id":"2301.02830","version":4},"attestation_state":"computed","paper":{"title":"Image Data Augmentation Approaches: A Comprehensive Survey and Future directions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Alessandra Mileo, Malika Bendechache, Rob Brennan, Teerath Kumar","submitted_at":"2023-01-07T11:37:32Z","abstract_excerpt":"Deep learning (DL) algorithms have shown significant performance in various computer vision tasks. However, having limited labelled data lead to a network overfitting problem, where network performance is bad on unseen data as compared to training data. Consequently, it limits performance improvement. To cope with this problem, various techniques have been proposed such as dropout, normalization and advanced data augmentation. Among these, data augmentation, which aims to enlarge the dataset size by including sample diversity, has been a hot topic in recent times. In this article, we focus on "},"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":"2301.02830","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-01-07T11:37:32Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"63164527938bc28e611b683011120529225ee4c0ee04f82255fa0a01516b49de","abstract_canon_sha256":"8a7ddde15d8ddab588305b406e4e8ddecb4e2f24dc8ee57524b2d2a32f5981b0"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:50:12.488361Z","signature_b64":"1Y4XhH0rLoAZggT+UgMJG+hMc+DbcHY5kaQxXB11sco234nq9HZYeY8382FBoIoKVct9At7qAgvRVZYiyYCMCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6eb1f2d2c20417b701eae8e36994df53332cd8e79a9811293e548e9d60641836","last_reissued_at":"2026-07-05T05:50:12.487914Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:50:12.487914Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Image Data Augmentation Approaches: A Comprehensive Survey and Future directions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CV","authors_text":"Alessandra Mileo, Malika Bendechache, Rob Brennan, Teerath Kumar","submitted_at":"2023-01-07T11:37:32Z","abstract_excerpt":"Deep learning (DL) algorithms have shown significant performance in various computer vision tasks. However, having limited labelled data lead to a network overfitting problem, where network performance is bad on unseen data as compared to training data. Consequently, it limits performance improvement. To cope with this problem, various techniques have been proposed such as dropout, normalization and advanced data augmentation. Among these, data augmentation, which aims to enlarge the dataset size by including sample diversity, has been a hot topic in recent times. In this article, we focus on "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.02830","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/2301.02830/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2301.02830","created_at":"2026-07-05T05:50:12.487972+00:00"},{"alias_kind":"arxiv_version","alias_value":"2301.02830v4","created_at":"2026-07-05T05:50:12.487972+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.02830","created_at":"2026-07-05T05:50:12.487972+00:00"},{"alias_kind":"pith_short_12","alias_value":"N2Y7FUWCAQL3","created_at":"2026-07-05T05:50:12.487972+00:00"},{"alias_kind":"pith_short_16","alias_value":"N2Y7FUWCAQL3OAPK","created_at":"2026-07-05T05:50:12.487972+00:00"},{"alias_kind":"pith_short_8","alias_value":"N2Y7FUWC","created_at":"2026-07-05T05:50:12.487972+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.25324","citing_title":"Unsupervised learning for the systematic identification of nondispersive wave packets in driven helium","ref_index":34,"is_internal_anchor":false},{"citing_arxiv_id":"2603.10146","citing_title":"Polarized Target Nuclear Magnetic Resonance Measurements with Deep Neural Networks","ref_index":21,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09425","citing_title":"AtteConDA: Attention-Based Conflict Suppression in Multi-Condition Diffusion Models and Synthetic Data Augmentation","ref_index":42,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/N2Y7FUWCAQL3OAPK5DRWTFG7KM","json":"https://pith.science/pith/N2Y7FUWCAQL3OAPK5DRWTFG7KM.json","graph_json":"https://pith.science/api/pith-number/N2Y7FUWCAQL3OAPK5DRWTFG7KM/graph.json","events_json":"https://pith.science/api/pith-number/N2Y7FUWCAQL3OAPK5DRWTFG7KM/events.json","paper":"https://pith.science/paper/N2Y7FUWC"},"agent_actions":{"view_html":"https://pith.science/pith/N2Y7FUWCAQL3OAPK5DRWTFG7KM","download_json":"https://pith.science/pith/N2Y7FUWCAQL3OAPK5DRWTFG7KM.json","view_paper":"https://pith.science/paper/N2Y7FUWC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2301.02830&json=true","fetch_graph":"https://pith.science/api/pith-number/N2Y7FUWCAQL3OAPK5DRWTFG7KM/graph.json","fetch_events":"https://pith.science/api/pith-number/N2Y7FUWCAQL3OAPK5DRWTFG7KM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/N2Y7FUWCAQL3OAPK5DRWTFG7KM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/N2Y7FUWCAQL3OAPK5DRWTFG7KM/action/storage_attestation","attest_author":"https://pith.science/pith/N2Y7FUWCAQL3OAPK5DRWTFG7KM/action/author_attestation","sign_citation":"https://pith.science/pith/N2Y7FUWCAQL3OAPK5DRWTFG7KM/action/citation_signature","submit_replication":"https://pith.science/pith/N2Y7FUWCAQL3OAPK5DRWTFG7KM/action/replication_record"}},"created_at":"2026-07-05T05:50:12.487972+00:00","updated_at":"2026-07-05T05:50:12.487972+00:00"}