{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:YTXAHXG4TMHTRI3SYZGRLET7ZK","short_pith_number":"pith:YTXAHXG4","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"},"canonical_sha256":"c4ee03dcdc9b0f38a372c64d15927fcaa609b4da94c84d6da0f5515d7782bea1","source":{"kind":"arxiv","id":"1802.02568","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.02568","created_at":"2026-05-18T00:24:04Z"},{"alias_kind":"arxiv_version","alias_value":"1802.02568v1","created_at":"2026-05-18T00:24:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.02568","created_at":"2026-05-18T00:24:04Z"},{"alias_kind":"pith_short_12","alias_value":"YTXAHXG4TMHT","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_16","alias_value":"YTXAHXG4TMHTRI3S","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_8","alias_value":"YTXAHXG4","created_at":"2026-05-18T12:33:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:YTXAHXG4TMHTRI3SYZGRLET7ZK","target":"record","payload":{"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"},"canonical_sha256":"c4ee03dcdc9b0f38a372c64d15927fcaa609b4da94c84d6da0f5515d7782bea1","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"},"source_kind":"arxiv","source_id":"1802.02568","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:24:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oF48DpxiNZzvxZe8HMT/9CVeG9PIZQnES714nkuXbc/nYnAWtOArw2eH+5zsCijLtQwPdOL/pUyQgyAgjFrXBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T12:48:13.394700Z"},"content_sha256":"21401b61338bb13595d8addc68e4673bf8cb88188ae7847233f44181cde7dffd","schema_version":"1.0","event_id":"sha256:21401b61338bb13595d8addc68e4673bf8cb88188ae7847233f44181cde7dffd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:YTXAHXG4TMHTRI3SYZGRLET7ZK","target":"graph","payload":{"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:24:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"DHwG56IVRYzUq1sigbZd8ikeBhgU4tyfuoQ2/m9G7wDn9D3DxUHJKkwjABXFHeDbGgtBFFUFMa8UR9CSxD1cAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-04T12:48:13.395044Z"},"content_sha256":"beb28bc2ebae432e02d0bb7887ba78787ae624f2aa2ac4f2305ecc06257c7bd9","schema_version":"1.0","event_id":"sha256:beb28bc2ebae432e02d0bb7887ba78787ae624f2aa2ac4f2305ecc06257c7bd9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YTXAHXG4TMHTRI3SYZGRLET7ZK/bundle.json","state_url":"https://pith.science/pith/YTXAHXG4TMHTRI3SYZGRLET7ZK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YTXAHXG4TMHTRI3SYZGRLET7ZK/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":"2026-07-04T12:48:13Z","links":{"resolver":"https://pith.science/pith/YTXAHXG4TMHTRI3SYZGRLET7ZK","bundle":"https://pith.science/pith/YTXAHXG4TMHTRI3SYZGRLET7ZK/bundle.json","state":"https://pith.science/pith/YTXAHXG4TMHTRI3SYZGRLET7ZK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YTXAHXG4TMHTRI3SYZGRLET7ZK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:YTXAHXG4TMHTRI3SYZGRLET7ZK","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":"66c581ea98c9abeeb3a077b39e2c8ec07652bb9fdd6926d6a9d07104efd86dbe","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-07T18:55:01Z","title_canon_sha256":"448ea0c689267fbb0758018da341167a6a2794e277a21841c27cd56267f15b2e"},"schema_version":"1.0","source":{"id":"1802.02568","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.02568","created_at":"2026-05-18T00:24:04Z"},{"alias_kind":"arxiv_version","alias_value":"1802.02568v1","created_at":"2026-05-18T00:24:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.02568","created_at":"2026-05-18T00:24:04Z"},{"alias_kind":"pith_short_12","alias_value":"YTXAHXG4TMHT","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_16","alias_value":"YTXAHXG4TMHTRI3S","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_8","alias_value":"YTXAHXG4","created_at":"2026-05-18T12:33:04Z"}],"graph_snapshots":[{"event_id":"sha256:beb28bc2ebae432e02d0bb7887ba78787ae624f2aa2ac4f2305ecc06257c7bd9","target":"graph","created_at":"2026-05-18T00:24:04Z","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"},"paper":{"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","authors_text":"Hamid Izadinia, Pierre Garrigues","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-07T18:55:01Z","title":"VISER: Visual Self-Regularization"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.02568","kind":"arxiv","version":1},"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":"sha256:21401b61338bb13595d8addc68e4673bf8cb88188ae7847233f44181cde7dffd","target":"record","created_at":"2026-05-18T00:24:04Z","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":"66c581ea98c9abeeb3a077b39e2c8ec07652bb9fdd6926d6a9d07104efd86dbe","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2018-02-07T18:55:01Z","title_canon_sha256":"448ea0c689267fbb0758018da341167a6a2794e277a21841c27cd56267f15b2e"},"schema_version":"1.0","source":{"id":"1802.02568","kind":"arxiv","version":1}},"canonical_sha256":"c4ee03dcdc9b0f38a372c64d15927fcaa609b4da94c84d6da0f5515d7782bea1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c4ee03dcdc9b0f38a372c64d15927fcaa609b4da94c84d6da0f5515d7782bea1","first_computed_at":"2026-05-18T00:24:04.882381Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:24:04.882381Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"xq5Tn05WlojzwwJPqaAXGP7F3ZnPnStk0KEKDNQ/pl6YqeqJnbqNWg0YTO4oJZNhmZ4qAnw5eJ3XZ1foCrHEBw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:24:04.882877Z","signed_message":"canonical_sha256_bytes"},"source_id":"1802.02568","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:21401b61338bb13595d8addc68e4673bf8cb88188ae7847233f44181cde7dffd","sha256:beb28bc2ebae432e02d0bb7887ba78787ae624f2aa2ac4f2305ecc06257c7bd9"],"state_sha256":"51b4f8579d63872f48d3803ce1602ea4bddc01c22946408857cfc0845ef450df"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"K1VSHaT3UfkpBusYpJDbga8dsdtmd+J23NetfbHWPWFLRe5HEitcgRIoh3gJrM25n+/TRuSv+VdJPdEmrNqIBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-04T12:48:13.396984Z","bundle_sha256":"7d0ea333bf6d64f087aa78ef2767282502b11d802b4214a3d77fa475f96f4e00"}}