{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:YTXAHXG4TMHTRI3SYZGRLET7ZK","short_pith_number":"pith:YTXAHXG4","schema_version":"1.0","canonical_sha256":"c4ee03dcdc9b0f38a372c64d15927fcaa609b4da94c84d6da0f5515d7782bea1","source":{"kind":"arxiv","id":"1802.02568","version":1},"attestation_state":"computed","paper":{"title":"VISER: Visual Self-Regularization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Hamid Izadinia, Pierre Garrigues","submitted_at":"2018-02-07T18:55:01Z","abstract_excerpt":"In this work, we propose the use of large set of unlabeled images as a source of regularization data for learning robust visual representation. Given a visual model trained by a labeled dataset in a supervised fashion, we augment our training samples by incorporating large number of unlabeled data and train a semi-supervised model. We demonstrate that our proposed learning approach leverages an abundance of unlabeled images and boosts the visual recognition performance which alleviates the need to rely on large labeled datasets for learning robust representation. To increment the number of ima"},"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":"1802.02568","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-07T18:55:01Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"448ea0c689267fbb0758018da341167a6a2794e277a21841c27cd56267f15b2e","abstract_canon_sha256":"66c581ea98c9abeeb3a077b39e2c8ec07652bb9fdd6926d6a9d07104efd86dbe"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:24:04.882877Z","signature_b64":"xq5Tn05WlojzwwJPqaAXGP7F3ZnPnStk0KEKDNQ/pl6YqeqJnbqNWg0YTO4oJZNhmZ4qAnw5eJ3XZ1foCrHEBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c4ee03dcdc9b0f38a372c64d15927fcaa609b4da94c84d6da0f5515d7782bea1","last_reissued_at":"2026-05-18T00:24:04.882381Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:24:04.882381Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"VISER: Visual Self-Regularization","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Hamid Izadinia, Pierre Garrigues","submitted_at":"2018-02-07T18:55:01Z","abstract_excerpt":"In this work, we propose the use of large set of unlabeled images as a source of regularization data for learning robust visual representation. Given a visual model trained by a labeled dataset in a supervised fashion, we augment our training samples by incorporating large number of unlabeled data and train a semi-supervised model. We demonstrate that our proposed learning approach leverages an abundance of unlabeled images and boosts the visual recognition performance which alleviates the need to rely on large labeled datasets for learning robust representation. To increment the number of ima"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.02568","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":""},"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":"1802.02568","created_at":"2026-05-18T00:24:04.882452+00:00"},{"alias_kind":"arxiv_version","alias_value":"1802.02568v1","created_at":"2026-05-18T00:24:04.882452+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.02568","created_at":"2026-05-18T00:24:04.882452+00:00"},{"alias_kind":"pith_short_12","alias_value":"YTXAHXG4TMHT","created_at":"2026-05-18T12:33:04.347982+00:00"},{"alias_kind":"pith_short_16","alias_value":"YTXAHXG4TMHTRI3S","created_at":"2026-05-18T12:33:04.347982+00:00"},{"alias_kind":"pith_short_8","alias_value":"YTXAHXG4","created_at":"2026-05-18T12:33:04.347982+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/YTXAHXG4TMHTRI3SYZGRLET7ZK","json":"https://pith.science/pith/YTXAHXG4TMHTRI3SYZGRLET7ZK.json","graph_json":"https://pith.science/api/pith-number/YTXAHXG4TMHTRI3SYZGRLET7ZK/graph.json","events_json":"https://pith.science/api/pith-number/YTXAHXG4TMHTRI3SYZGRLET7ZK/events.json","paper":"https://pith.science/paper/YTXAHXG4"},"agent_actions":{"view_html":"https://pith.science/pith/YTXAHXG4TMHTRI3SYZGRLET7ZK","download_json":"https://pith.science/pith/YTXAHXG4TMHTRI3SYZGRLET7ZK.json","view_paper":"https://pith.science/paper/YTXAHXG4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1802.02568&json=true","fetch_graph":"https://pith.science/api/pith-number/YTXAHXG4TMHTRI3SYZGRLET7ZK/graph.json","fetch_events":"https://pith.science/api/pith-number/YTXAHXG4TMHTRI3SYZGRLET7ZK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YTXAHXG4TMHTRI3SYZGRLET7ZK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YTXAHXG4TMHTRI3SYZGRLET7ZK/action/storage_attestation","attest_author":"https://pith.science/pith/YTXAHXG4TMHTRI3SYZGRLET7ZK/action/author_attestation","sign_citation":"https://pith.science/pith/YTXAHXG4TMHTRI3SYZGRLET7ZK/action/citation_signature","submit_replication":"https://pith.science/pith/YTXAHXG4TMHTRI3SYZGRLET7ZK/action/replication_record"}},"created_at":"2026-05-18T00:24:04.882452+00:00","updated_at":"2026-05-18T00:24:04.882452+00:00"}