{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:DAWM2H3HIO4KFCLIFJ2EK2G2VQ","short_pith_number":"pith:DAWM2H3H","schema_version":"1.0","canonical_sha256":"182ccd1f6743b8a289682a744568daac06388158c40b76809daaa7f12947681d","source":{"kind":"arxiv","id":"2505.17883","version":1},"attestation_state":"computed","paper":{"title":"FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Joachim Denzler, Julia Niebling, Laines Schmalwasser, Niklas Penzel","submitted_at":"2025-05-23T13:31:54Z","abstract_excerpt":"Concepts such as objects, patterns, and shapes are how humans understand the world. Building on this intuition, concept-based explainability methods aim to study representations learned by deep neural networks in relation to human-understandable concepts. Here, Concept Activation Vectors (CAVs) are an important tool and can identify whether a model learned a concept or not. However, the computational cost and time requirements of existing CAV computation pose a significant challenge, particularly in large-scale, high-dimensional architectures. To address this limitation, we introduce FastCAV, "},"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":"2505.17883","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2025-05-23T13:31:54Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"bdc999284fce3d801f2d95646ae68e08fa5d086359c1242c2e4ebb8150ce0419","abstract_canon_sha256":"c0644d8e610a95128ce7c89da57d039b1e4d973995f08beac063f927efa88636"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:08:33.286322Z","signature_b64":"Y+c5wBlEPFdD8p2Jc2qWcZFb38w4aAxbnlOh4dLnnMLiKipwtCmU99ym5ViLmvvR3jarsulI1iT7bvlD/j+IDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"182ccd1f6743b8a289682a744568daac06388158c40b76809daaa7f12947681d","last_reissued_at":"2026-07-05T11:08:33.285765Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:08:33.285765Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"FastCAV: Efficient Computation of Concept Activation Vectors for Explaining Deep Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"cs.LG","authors_text":"Joachim Denzler, Julia Niebling, Laines Schmalwasser, Niklas Penzel","submitted_at":"2025-05-23T13:31:54Z","abstract_excerpt":"Concepts such as objects, patterns, and shapes are how humans understand the world. Building on this intuition, concept-based explainability methods aim to study representations learned by deep neural networks in relation to human-understandable concepts. Here, Concept Activation Vectors (CAVs) are an important tool and can identify whether a model learned a concept or not. However, the computational cost and time requirements of existing CAV computation pose a significant challenge, particularly in large-scale, high-dimensional architectures. To address this limitation, we introduce FastCAV, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2505.17883","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/2505.17883/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":"2505.17883","created_at":"2026-07-05T11:08:33.285850+00:00"},{"alias_kind":"arxiv_version","alias_value":"2505.17883v1","created_at":"2026-07-05T11:08:33.285850+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2505.17883","created_at":"2026-07-05T11:08:33.285850+00:00"},{"alias_kind":"pith_short_12","alias_value":"DAWM2H3HIO4K","created_at":"2026-07-05T11:08:33.285850+00:00"},{"alias_kind":"pith_short_16","alias_value":"DAWM2H3HIO4KFCLI","created_at":"2026-07-05T11:08:33.285850+00:00"},{"alias_kind":"pith_short_8","alias_value":"DAWM2H3H","created_at":"2026-07-05T11:08:33.285850+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.10261","citing_title":"E-TCAV: Formalizing Penultimate Proxies for Efficient Concept Based Interpretability","ref_index":13,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/DAWM2H3HIO4KFCLIFJ2EK2G2VQ","json":"https://pith.science/pith/DAWM2H3HIO4KFCLIFJ2EK2G2VQ.json","graph_json":"https://pith.science/api/pith-number/DAWM2H3HIO4KFCLIFJ2EK2G2VQ/graph.json","events_json":"https://pith.science/api/pith-number/DAWM2H3HIO4KFCLIFJ2EK2G2VQ/events.json","paper":"https://pith.science/paper/DAWM2H3H"},"agent_actions":{"view_html":"https://pith.science/pith/DAWM2H3HIO4KFCLIFJ2EK2G2VQ","download_json":"https://pith.science/pith/DAWM2H3HIO4KFCLIFJ2EK2G2VQ.json","view_paper":"https://pith.science/paper/DAWM2H3H","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2505.17883&json=true","fetch_graph":"https://pith.science/api/pith-number/DAWM2H3HIO4KFCLIFJ2EK2G2VQ/graph.json","fetch_events":"https://pith.science/api/pith-number/DAWM2H3HIO4KFCLIFJ2EK2G2VQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/DAWM2H3HIO4KFCLIFJ2EK2G2VQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/DAWM2H3HIO4KFCLIFJ2EK2G2VQ/action/storage_attestation","attest_author":"https://pith.science/pith/DAWM2H3HIO4KFCLIFJ2EK2G2VQ/action/author_attestation","sign_citation":"https://pith.science/pith/DAWM2H3HIO4KFCLIFJ2EK2G2VQ/action/citation_signature","submit_replication":"https://pith.science/pith/DAWM2H3HIO4KFCLIFJ2EK2G2VQ/action/replication_record"}},"created_at":"2026-07-05T11:08:33.285850+00:00","updated_at":"2026-07-05T11:08:33.285850+00:00"}