{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:43XCKESIWOGVQHWPTS5L5FCBAB","short_pith_number":"pith:43XCKESI","schema_version":"1.0","canonical_sha256":"e6ee251248b38d581ecf9cbabe94410076e372f06a87784c8116f051195070ff","source":{"kind":"arxiv","id":"2103.12719","version":2},"attestation_state":"computed","paper":{"title":"Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Ari S. Morcos, Chaitanya K. Ryali, David J. Schwab","submitted_at":"2021-03-23T17:39:16Z","abstract_excerpt":"Recent progress in self-supervised learning has demonstrated promising results in multiple visual tasks. An important ingredient in high-performing self-supervised methods is the use of data augmentation by training models to place different augmented views of the same image nearby in embedding space. However, commonly used augmentation pipelines treat images holistically, ignoring the semantic relevance of parts of an image-e.g. a subject vs. a background-which can lead to the learning of spurious correlations. Our work addresses this problem by investigating a class of simple, yet highly eff"},"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":"2103.12719","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-03-23T17:39:16Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"5f844efd6765953599442e3f44426bbb3d0d1a9614d1e9e9369a60a342c5994b","abstract_canon_sha256":"df3a071b2b811764fda74c045292abf05a81e6aceafd54f878c61dc185aa99cc"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:31:13.252840Z","signature_b64":"Pi0187S3kZR1aSMBmI6LgjO6DT2iGkYsVjxU/nIVwlWB7zjkudh7lhs1ng5Js846gopWoRl9ttS+4y67/J6SCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e6ee251248b38d581ecf9cbabe94410076e372f06a87784c8116f051195070ff","last_reissued_at":"2026-07-05T03:31:13.251932Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:31:13.251932Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Characterizing and Improving the Robustness of Self-Supervised Learning through Background Augmentations","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Ari S. Morcos, Chaitanya K. Ryali, David J. Schwab","submitted_at":"2021-03-23T17:39:16Z","abstract_excerpt":"Recent progress in self-supervised learning has demonstrated promising results in multiple visual tasks. An important ingredient in high-performing self-supervised methods is the use of data augmentation by training models to place different augmented views of the same image nearby in embedding space. However, commonly used augmentation pipelines treat images holistically, ignoring the semantic relevance of parts of an image-e.g. a subject vs. a background-which can lead to the learning of spurious correlations. Our work addresses this problem by investigating a class of simple, yet highly eff"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2103.12719","kind":"arxiv","version":2},"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/2103.12719/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":"2103.12719","created_at":"2026-07-05T03:31:13.252400+00:00"},{"alias_kind":"arxiv_version","alias_value":"2103.12719v2","created_at":"2026-07-05T03:31:13.252400+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2103.12719","created_at":"2026-07-05T03:31:13.252400+00:00"},{"alias_kind":"pith_short_12","alias_value":"43XCKESIWOGV","created_at":"2026-07-05T03:31:13.252400+00:00"},{"alias_kind":"pith_short_16","alias_value":"43XCKESIWOGVQHWP","created_at":"2026-07-05T03:31:13.252400+00:00"},{"alias_kind":"pith_short_8","alias_value":"43XCKESI","created_at":"2026-07-05T03:31:13.252400+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/43XCKESIWOGVQHWPTS5L5FCBAB","json":"https://pith.science/pith/43XCKESIWOGVQHWPTS5L5FCBAB.json","graph_json":"https://pith.science/api/pith-number/43XCKESIWOGVQHWPTS5L5FCBAB/graph.json","events_json":"https://pith.science/api/pith-number/43XCKESIWOGVQHWPTS5L5FCBAB/events.json","paper":"https://pith.science/paper/43XCKESI"},"agent_actions":{"view_html":"https://pith.science/pith/43XCKESIWOGVQHWPTS5L5FCBAB","download_json":"https://pith.science/pith/43XCKESIWOGVQHWPTS5L5FCBAB.json","view_paper":"https://pith.science/paper/43XCKESI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2103.12719&json=true","fetch_graph":"https://pith.science/api/pith-number/43XCKESIWOGVQHWPTS5L5FCBAB/graph.json","fetch_events":"https://pith.science/api/pith-number/43XCKESIWOGVQHWPTS5L5FCBAB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/43XCKESIWOGVQHWPTS5L5FCBAB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/43XCKESIWOGVQHWPTS5L5FCBAB/action/storage_attestation","attest_author":"https://pith.science/pith/43XCKESIWOGVQHWPTS5L5FCBAB/action/author_attestation","sign_citation":"https://pith.science/pith/43XCKESIWOGVQHWPTS5L5FCBAB/action/citation_signature","submit_replication":"https://pith.science/pith/43XCKESIWOGVQHWPTS5L5FCBAB/action/replication_record"}},"created_at":"2026-07-05T03:31:13.252400+00:00","updated_at":"2026-07-05T03:31:13.252400+00:00"}