{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:W63RVKMIWTVYTB55EP3BV2OLQ2","short_pith_number":"pith:W63RVKMI","schema_version":"1.0","canonical_sha256":"b7b71aa988b4eb8987bd23f61ae9cb86989acb7b8365faa6db57e04425665aee","source":{"kind":"arxiv","id":"2411.06727","version":2},"attestation_state":"computed","paper":{"title":"Can KAN Work? Exploring the Potential of Kolmogorov-Arnold Networks in Computer Vision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Li Shi, Yueyang Cang, Yu hang liu","submitted_at":"2024-11-11T05:44:48Z","abstract_excerpt":"Kolmogorov-Arnold Networks(KANs), as a theoretically efficient neural network architecture, have garnered attention for their potential in capturing complex patterns. However, their application in computer vision remains relatively unexplored. This study first analyzes the potential of KAN in computer vision tasks, evaluating the performance of KAN and its convolutional variants in image classification and semantic segmentation. The focus is placed on examining their characteristics across varying data scales and noise levels. Results indicate that while KAN exhibits stronger fitting capabilit"},"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":"2411.06727","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-11-11T05:44:48Z","cross_cats_sorted":[],"title_canon_sha256":"8c1ba36f47b1fcb4ea9ef5b4c118aa8e5eb6d928f8f57d1e3087a0470a6d5e12","abstract_canon_sha256":"6e16e152f521b52334f48233585319c0d2637145af01ceca107cd7b1bb8cea19"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:35:14.753534Z","signature_b64":"wVzQLjIc8BdiqwgzyvWbMDbd8XlcD3PxD0lbmViY16LB9cwQVErj0Wd5opK5nL8WfyXpB/8hxyukgYKhY4F7Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b7b71aa988b4eb8987bd23f61ae9cb86989acb7b8365faa6db57e04425665aee","last_reissued_at":"2026-07-05T09:35:14.753050Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:35:14.753050Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Can KAN Work? Exploring the Potential of Kolmogorov-Arnold Networks in Computer Vision","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Li Shi, Yueyang Cang, Yu hang liu","submitted_at":"2024-11-11T05:44:48Z","abstract_excerpt":"Kolmogorov-Arnold Networks(KANs), as a theoretically efficient neural network architecture, have garnered attention for their potential in capturing complex patterns. However, their application in computer vision remains relatively unexplored. This study first analyzes the potential of KAN in computer vision tasks, evaluating the performance of KAN and its convolutional variants in image classification and semantic segmentation. The focus is placed on examining their characteristics across varying data scales and noise levels. Results indicate that while KAN exhibits stronger fitting capabilit"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.06727","kind":"arxiv","version":2},"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/2411.06727/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":"2411.06727","created_at":"2026-07-05T09:35:14.753115+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.06727v2","created_at":"2026-07-05T09:35:14.753115+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.06727","created_at":"2026-07-05T09:35:14.753115+00:00"},{"alias_kind":"pith_short_12","alias_value":"W63RVKMIWTVY","created_at":"2026-07-05T09:35:14.753115+00:00"},{"alias_kind":"pith_short_16","alias_value":"W63RVKMIWTVYTB55","created_at":"2026-07-05T09:35:14.753115+00:00"},{"alias_kind":"pith_short_8","alias_value":"W63RVKMI","created_at":"2026-07-05T09:35:14.753115+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":6,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.24371","citing_title":"Structural Kolmogorov-Arnold Convolutions: Learnable Function on the Values or the Filter Shape as Parameter-Efficient Alternative to Per-Edge Convolutional KANs","ref_index":7,"is_internal_anchor":false},{"citing_arxiv_id":"2606.23425","citing_title":"Interpretable Kolmogorov-Arnold Network with Feature-Isolated Temporal Attention Mechanism for Electricity Load Forecasting","ref_index":60,"is_internal_anchor":false},{"citing_arxiv_id":"2605.19031","citing_title":"KAN-MLP-Mixer: A comprehensive investigation of the usage of Kolmogorov-Arnold Networks (KANs) for improving IMU-based Human Activity Recognition","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2605.19031","citing_title":"KAN-MLP-Mixer: A comprehensive investigation of the usage of Kolmogorov-Arnold Networks (KANs) for improving IMU-based Human Activity Recognition","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09572","citing_title":"KAN Text to Vision? The Exploration of Kolmogorov-Arnold Networks for Multi-Scale Sequence-Based Pose Animation from Sign Language Notation","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2605.02190","citing_title":"KANs need curvature: penalties for compositional smoothness","ref_index":5,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/W63RVKMIWTVYTB55EP3BV2OLQ2","json":"https://pith.science/pith/W63RVKMIWTVYTB55EP3BV2OLQ2.json","graph_json":"https://pith.science/api/pith-number/W63RVKMIWTVYTB55EP3BV2OLQ2/graph.json","events_json":"https://pith.science/api/pith-number/W63RVKMIWTVYTB55EP3BV2OLQ2/events.json","paper":"https://pith.science/paper/W63RVKMI"},"agent_actions":{"view_html":"https://pith.science/pith/W63RVKMIWTVYTB55EP3BV2OLQ2","download_json":"https://pith.science/pith/W63RVKMIWTVYTB55EP3BV2OLQ2.json","view_paper":"https://pith.science/paper/W63RVKMI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.06727&json=true","fetch_graph":"https://pith.science/api/pith-number/W63RVKMIWTVYTB55EP3BV2OLQ2/graph.json","fetch_events":"https://pith.science/api/pith-number/W63RVKMIWTVYTB55EP3BV2OLQ2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/W63RVKMIWTVYTB55EP3BV2OLQ2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/W63RVKMIWTVYTB55EP3BV2OLQ2/action/storage_attestation","attest_author":"https://pith.science/pith/W63RVKMIWTVYTB55EP3BV2OLQ2/action/author_attestation","sign_citation":"https://pith.science/pith/W63RVKMIWTVYTB55EP3BV2OLQ2/action/citation_signature","submit_replication":"https://pith.science/pith/W63RVKMIWTVYTB55EP3BV2OLQ2/action/replication_record"}},"created_at":"2026-07-05T09:35:14.753115+00:00","updated_at":"2026-07-05T09:35:14.753115+00:00"}