{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:YVTZUSPKXMBG3WY6AQV76EY6TL","short_pith_number":"pith:YVTZUSPK","schema_version":"1.0","canonical_sha256":"c5679a49eabb026ddb1e042bff131e9ae0262227f1c9439b15dd3090b58039e9","source":{"kind":"arxiv","id":"2110.14613","version":1},"attestation_state":"computed","paper":{"title":"International Workshop on Continual Semi-Supervised Learning: Introduction, Benchmarks and Baselines","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Ajmal Shahbaz, Fabio Cuzzolin, Kevin Cannons, Mohammad Asiful Hossain, Salman Khan, Vincenzo Lomonaco, Zhan Xu","submitted_at":"2021-10-27T17:34:40Z","abstract_excerpt":"The aim of this paper is to formalize a new continual semi-supervised learning (CSSL) paradigm, proposed to the attention of the machine learning community via the IJCAI 2021 International Workshop on Continual Semi-Supervised Learning (CSSL-IJCAI), with the aim of raising field awareness about this problem and mobilizing its effort in this direction. After a formal definition of continual semi-supervised learning and the appropriate training and testing protocols, the paper introduces two new benchmarks specifically designed to assess CSSL on two important computer vision tasks: activity reco"},"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":"2110.14613","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-10-27T17:34:40Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"4aa6e03e2fe63aedf1f5e1a3555986cd8feab06fdba9ad2b5693f9b0661e5827","abstract_canon_sha256":"70e16e331f602e0518d3fd6b410e2b5469846547ab9a45b2b3271b7be0bdb3ca"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:26:30.186984Z","signature_b64":"iElMzcigqiFzBIvl7l9nZu139ckByE30k3hA+8zR/hUndOprDYzcf6p20J4u7iRSixbUUb0XHsA8p09m+D1IAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c5679a49eabb026ddb1e042bff131e9ae0262227f1c9439b15dd3090b58039e9","last_reissued_at":"2026-07-05T03:26:30.186575Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:26:30.186575Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"International Workshop on Continual Semi-Supervised Learning: Introduction, Benchmarks and Baselines","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Ajmal Shahbaz, Fabio Cuzzolin, Kevin Cannons, Mohammad Asiful Hossain, Salman Khan, Vincenzo Lomonaco, Zhan Xu","submitted_at":"2021-10-27T17:34:40Z","abstract_excerpt":"The aim of this paper is to formalize a new continual semi-supervised learning (CSSL) paradigm, proposed to the attention of the machine learning community via the IJCAI 2021 International Workshop on Continual Semi-Supervised Learning (CSSL-IJCAI), with the aim of raising field awareness about this problem and mobilizing its effort in this direction. After a formal definition of continual semi-supervised learning and the appropriate training and testing protocols, the paper introduces two new benchmarks specifically designed to assess CSSL on two important computer vision tasks: activity reco"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2110.14613","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/2110.14613/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":"2110.14613","created_at":"2026-07-05T03:26:30.186631+00:00"},{"alias_kind":"arxiv_version","alias_value":"2110.14613v1","created_at":"2026-07-05T03:26:30.186631+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2110.14613","created_at":"2026-07-05T03:26:30.186631+00:00"},{"alias_kind":"pith_short_12","alias_value":"YVTZUSPKXMBG","created_at":"2026-07-05T03:26:30.186631+00:00"},{"alias_kind":"pith_short_16","alias_value":"YVTZUSPKXMBG3WY6","created_at":"2026-07-05T03:26:30.186631+00:00"},{"alias_kind":"pith_short_8","alias_value":"YVTZUSPK","created_at":"2026-07-05T03:26:30.186631+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.07539","citing_title":"Anomaly detection using Diffusion-based methods","ref_index":17,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YVTZUSPKXMBG3WY6AQV76EY6TL","json":"https://pith.science/pith/YVTZUSPKXMBG3WY6AQV76EY6TL.json","graph_json":"https://pith.science/api/pith-number/YVTZUSPKXMBG3WY6AQV76EY6TL/graph.json","events_json":"https://pith.science/api/pith-number/YVTZUSPKXMBG3WY6AQV76EY6TL/events.json","paper":"https://pith.science/paper/YVTZUSPK"},"agent_actions":{"view_html":"https://pith.science/pith/YVTZUSPKXMBG3WY6AQV76EY6TL","download_json":"https://pith.science/pith/YVTZUSPKXMBG3WY6AQV76EY6TL.json","view_paper":"https://pith.science/paper/YVTZUSPK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2110.14613&json=true","fetch_graph":"https://pith.science/api/pith-number/YVTZUSPKXMBG3WY6AQV76EY6TL/graph.json","fetch_events":"https://pith.science/api/pith-number/YVTZUSPKXMBG3WY6AQV76EY6TL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YVTZUSPKXMBG3WY6AQV76EY6TL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YVTZUSPKXMBG3WY6AQV76EY6TL/action/storage_attestation","attest_author":"https://pith.science/pith/YVTZUSPKXMBG3WY6AQV76EY6TL/action/author_attestation","sign_citation":"https://pith.science/pith/YVTZUSPKXMBG3WY6AQV76EY6TL/action/citation_signature","submit_replication":"https://pith.science/pith/YVTZUSPKXMBG3WY6AQV76EY6TL/action/replication_record"}},"created_at":"2026-07-05T03:26:30.186631+00:00","updated_at":"2026-07-05T03:26:30.186631+00:00"}