{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:AVSM7F3FBXS7DKSKMD25O7KGI7","short_pith_number":"pith:AVSM7F3F","schema_version":"1.0","canonical_sha256":"0564cf97650de5f1aa4a60f5d77d4647ebacc5d4c938e76bf6608c6f04a5f8d8","source":{"kind":"arxiv","id":"2207.10023","version":1},"attestation_state":"computed","paper":{"title":"Tailoring Self-Supervision for Supervised Learning","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jae-Pil Heo, Ji-Hwan Kim, WonJun Moon","submitted_at":"2022-07-20T16:41:14Z","abstract_excerpt":"Recently, it is shown that deploying a proper self-supervision is a prospective way to enhance the performance of supervised learning. Yet, the benefits of self-supervision are not fully exploited as previous pretext tasks are specialized for unsupervised representation learning. To this end, we begin by presenting three desirable properties for such auxiliary tasks to assist the supervised objective. First, the tasks need to guide the model to learn rich features. Second, the transformations involved in the self-supervision should not significantly alter the training distribution. Third, the "},"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":"2207.10023","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2022-07-20T16:41:14Z","cross_cats_sorted":[],"title_canon_sha256":"0873a144d6a0f4508554a339403dbf8223c7b9329c8f9b1687e37880bac27619","abstract_canon_sha256":"8807ec4fd5f61d9f30ed4b7b1d6a03b58bad92017c138a36afb66a2048a28ed5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:42:10.488628Z","signature_b64":"/6yvjL1dtU5pVrendQh6Q2B3DaOLzeeQN/6NhtBqB4yXZsjUnS84v9bV2J+7MbbkfJi/E9smgGWKWmFZwgqPCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0564cf97650de5f1aa4a60f5d77d4647ebacc5d4c938e76bf6608c6f04a5f8d8","last_reissued_at":"2026-07-05T04:42:10.488217Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:42:10.488217Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Tailoring Self-Supervision for Supervised Learning","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Jae-Pil Heo, Ji-Hwan Kim, WonJun Moon","submitted_at":"2022-07-20T16:41:14Z","abstract_excerpt":"Recently, it is shown that deploying a proper self-supervision is a prospective way to enhance the performance of supervised learning. Yet, the benefits of self-supervision are not fully exploited as previous pretext tasks are specialized for unsupervised representation learning. To this end, we begin by presenting three desirable properties for such auxiliary tasks to assist the supervised objective. First, the tasks need to guide the model to learn rich features. Second, the transformations involved in the self-supervision should not significantly alter the training distribution. Third, the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2207.10023","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/2207.10023/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":"2207.10023","created_at":"2026-07-05T04:42:10.488271+00:00"},{"alias_kind":"arxiv_version","alias_value":"2207.10023v1","created_at":"2026-07-05T04:42:10.488271+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2207.10023","created_at":"2026-07-05T04:42:10.488271+00:00"},{"alias_kind":"pith_short_12","alias_value":"AVSM7F3FBXS7","created_at":"2026-07-05T04:42:10.488271+00:00"},{"alias_kind":"pith_short_16","alias_value":"AVSM7F3FBXS7DKSK","created_at":"2026-07-05T04:42:10.488271+00:00"},{"alias_kind":"pith_short_8","alias_value":"AVSM7F3F","created_at":"2026-07-05T04:42:10.488271+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/AVSM7F3FBXS7DKSKMD25O7KGI7","json":"https://pith.science/pith/AVSM7F3FBXS7DKSKMD25O7KGI7.json","graph_json":"https://pith.science/api/pith-number/AVSM7F3FBXS7DKSKMD25O7KGI7/graph.json","events_json":"https://pith.science/api/pith-number/AVSM7F3FBXS7DKSKMD25O7KGI7/events.json","paper":"https://pith.science/paper/AVSM7F3F"},"agent_actions":{"view_html":"https://pith.science/pith/AVSM7F3FBXS7DKSKMD25O7KGI7","download_json":"https://pith.science/pith/AVSM7F3FBXS7DKSKMD25O7KGI7.json","view_paper":"https://pith.science/paper/AVSM7F3F","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2207.10023&json=true","fetch_graph":"https://pith.science/api/pith-number/AVSM7F3FBXS7DKSKMD25O7KGI7/graph.json","fetch_events":"https://pith.science/api/pith-number/AVSM7F3FBXS7DKSKMD25O7KGI7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AVSM7F3FBXS7DKSKMD25O7KGI7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AVSM7F3FBXS7DKSKMD25O7KGI7/action/storage_attestation","attest_author":"https://pith.science/pith/AVSM7F3FBXS7DKSKMD25O7KGI7/action/author_attestation","sign_citation":"https://pith.science/pith/AVSM7F3FBXS7DKSKMD25O7KGI7/action/citation_signature","submit_replication":"https://pith.science/pith/AVSM7F3FBXS7DKSKMD25O7KGI7/action/replication_record"}},"created_at":"2026-07-05T04:42:10.488271+00:00","updated_at":"2026-07-05T04:42:10.488271+00:00"}