{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:CQBLE6X3MDLWIBQJ7EDBNN7N25","short_pith_number":"pith:CQBLE6X3","schema_version":"1.0","canonical_sha256":"1402b27afb60d7640609f90616b7edd75fb7eac33f33ae35b286b82b0cea25c3","source":{"kind":"arxiv","id":"2308.12366","version":1},"attestation_state":"computed","paper":{"title":"Continual Zero-Shot Learning through Semantically Guided Generative Random Walks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ivan Skorokhodov, Kai Yi, Mohamed Elhoseiny, Paul Janson, Wenxuan Zhang","submitted_at":"2023-08-23T18:10:12Z","abstract_excerpt":"Learning novel concepts, remembering previous knowledge, and adapting it to future tasks occur simultaneously throughout a human's lifetime. To model such comprehensive abilities, continual zero-shot learning (CZSL) has recently been introduced. However, most existing methods overused unseen semantic information that may not be continually accessible in realistic settings. In this paper, we address the challenge of continual zero-shot learning where unseen information is not provided during training, by leveraging generative modeling. The heart of the generative-based methods is to learn quali"},"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":"2308.12366","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-08-23T18:10:12Z","cross_cats_sorted":[],"title_canon_sha256":"2977dd2d4fce949af5588e28ab97c68c610dd3aad304e5ae6a7d0ca425436d28","abstract_canon_sha256":"a4bdd219b2413d180e0635386e5cbbf50c0f3952c1ff15731901d0ed6cd10578"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:44:11.247225Z","signature_b64":"M9u4W1qb4tCZGCJ1DqvHOlooyLXuiZ4PUToqkIuqqOXXVg506z9UcL1M235c8zPqJiNXqeRnc1OBQSWuqKxgCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1402b27afb60d7640609f90616b7edd75fb7eac33f33ae35b286b82b0cea25c3","last_reissued_at":"2026-07-05T06:44:11.246702Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:44:11.246702Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Continual Zero-Shot Learning through Semantically Guided Generative Random Walks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Ivan Skorokhodov, Kai Yi, Mohamed Elhoseiny, Paul Janson, Wenxuan Zhang","submitted_at":"2023-08-23T18:10:12Z","abstract_excerpt":"Learning novel concepts, remembering previous knowledge, and adapting it to future tasks occur simultaneously throughout a human's lifetime. To model such comprehensive abilities, continual zero-shot learning (CZSL) has recently been introduced. However, most existing methods overused unseen semantic information that may not be continually accessible in realistic settings. In this paper, we address the challenge of continual zero-shot learning where unseen information is not provided during training, by leveraging generative modeling. The heart of the generative-based methods is to learn quali"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2308.12366","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/2308.12366/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":"2308.12366","created_at":"2026-07-05T06:44:11.246769+00:00"},{"alias_kind":"arxiv_version","alias_value":"2308.12366v1","created_at":"2026-07-05T06:44:11.246769+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2308.12366","created_at":"2026-07-05T06:44:11.246769+00:00"},{"alias_kind":"pith_short_12","alias_value":"CQBLE6X3MDLW","created_at":"2026-07-05T06:44:11.246769+00:00"},{"alias_kind":"pith_short_16","alias_value":"CQBLE6X3MDLWIBQJ","created_at":"2026-07-05T06:44:11.246769+00:00"},{"alias_kind":"pith_short_8","alias_value":"CQBLE6X3","created_at":"2026-07-05T06:44:11.246769+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/CQBLE6X3MDLWIBQJ7EDBNN7N25","json":"https://pith.science/pith/CQBLE6X3MDLWIBQJ7EDBNN7N25.json","graph_json":"https://pith.science/api/pith-number/CQBLE6X3MDLWIBQJ7EDBNN7N25/graph.json","events_json":"https://pith.science/api/pith-number/CQBLE6X3MDLWIBQJ7EDBNN7N25/events.json","paper":"https://pith.science/paper/CQBLE6X3"},"agent_actions":{"view_html":"https://pith.science/pith/CQBLE6X3MDLWIBQJ7EDBNN7N25","download_json":"https://pith.science/pith/CQBLE6X3MDLWIBQJ7EDBNN7N25.json","view_paper":"https://pith.science/paper/CQBLE6X3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2308.12366&json=true","fetch_graph":"https://pith.science/api/pith-number/CQBLE6X3MDLWIBQJ7EDBNN7N25/graph.json","fetch_events":"https://pith.science/api/pith-number/CQBLE6X3MDLWIBQJ7EDBNN7N25/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/CQBLE6X3MDLWIBQJ7EDBNN7N25/action/timestamp_anchor","attest_storage":"https://pith.science/pith/CQBLE6X3MDLWIBQJ7EDBNN7N25/action/storage_attestation","attest_author":"https://pith.science/pith/CQBLE6X3MDLWIBQJ7EDBNN7N25/action/author_attestation","sign_citation":"https://pith.science/pith/CQBLE6X3MDLWIBQJ7EDBNN7N25/action/citation_signature","submit_replication":"https://pith.science/pith/CQBLE6X3MDLWIBQJ7EDBNN7N25/action/replication_record"}},"created_at":"2026-07-05T06:44:11.246769+00:00","updated_at":"2026-07-05T06:44:11.246769+00:00"}