{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:PITCLEQ5JPXAVCE257OORYRKZC","short_pith_number":"pith:PITCLEQ5","schema_version":"1.0","canonical_sha256":"7a2625921d4bee0a889aefdce8e22ac8a1a4c2da0fa4ea32305af2828297e2eb","source":{"kind":"arxiv","id":"2412.11657","version":3},"attestation_state":"computed","paper":{"title":"CNNtention: Can CNNs do better with Attention?","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Julian Glattki, Nikhil Kapila, Tejas Rathi","submitted_at":"2024-12-16T11:00:02Z","abstract_excerpt":"Convolutional Neural Networks (CNNs) have been the standard for image classification tasks for a long time, but more recently attention-based mechanisms have gained traction. This project aims to compare traditional CNNs with attention-augmented CNNs across an image classification task. By evaluating and comparing their performance, accuracy and computational efficiency, the project will highlight benefits and trade-off of the localized feature extraction of traditional CNNs and the global context capture in attention-augmented CNNs. By doing this, we can reveal further insights into their res"},"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":"2412.11657","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2024-12-16T11:00:02Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"48e6693c6a50637c0b4a294c2fde8adcef48fcb1067cb8adbcd201912acbe77e","abstract_canon_sha256":"84598837f9f49a679215a3e65179ae2b920333f3db0e7aca99cc7511f26768cb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:55:14.914852Z","signature_b64":"7tg2KBA12E6FHJtPV45x4ciSAjBVpiOSDugn57VGZC6AEZURm6Fam7LOu4bcAIxMEcD8088V570I9NFwuFJiDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7a2625921d4bee0a889aefdce8e22ac8a1a4c2da0fa4ea32305af2828297e2eb","last_reissued_at":"2026-07-05T09:55:14.914446Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:55:14.914446Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"CNNtention: Can CNNs do better with Attention?","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Julian Glattki, Nikhil Kapila, Tejas Rathi","submitted_at":"2024-12-16T11:00:02Z","abstract_excerpt":"Convolutional Neural Networks (CNNs) have been the standard for image classification tasks for a long time, but more recently attention-based mechanisms have gained traction. This project aims to compare traditional CNNs with attention-augmented CNNs across an image classification task. By evaluating and comparing their performance, accuracy and computational efficiency, the project will highlight benefits and trade-off of the localized feature extraction of traditional CNNs and the global context capture in attention-augmented CNNs. By doing this, we can reveal further insights into their res"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.11657","kind":"arxiv","version":3},"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/2412.11657/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":"2412.11657","created_at":"2026-07-05T09:55:14.914503+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.11657v3","created_at":"2026-07-05T09:55:14.914503+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.11657","created_at":"2026-07-05T09:55:14.914503+00:00"},{"alias_kind":"pith_short_12","alias_value":"PITCLEQ5JPXA","created_at":"2026-07-05T09:55:14.914503+00:00"},{"alias_kind":"pith_short_16","alias_value":"PITCLEQ5JPXAVCE2","created_at":"2026-07-05T09:55:14.914503+00:00"},{"alias_kind":"pith_short_8","alias_value":"PITCLEQ5","created_at":"2026-07-05T09:55:14.914503+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/PITCLEQ5JPXAVCE257OORYRKZC","json":"https://pith.science/pith/PITCLEQ5JPXAVCE257OORYRKZC.json","graph_json":"https://pith.science/api/pith-number/PITCLEQ5JPXAVCE257OORYRKZC/graph.json","events_json":"https://pith.science/api/pith-number/PITCLEQ5JPXAVCE257OORYRKZC/events.json","paper":"https://pith.science/paper/PITCLEQ5"},"agent_actions":{"view_html":"https://pith.science/pith/PITCLEQ5JPXAVCE257OORYRKZC","download_json":"https://pith.science/pith/PITCLEQ5JPXAVCE257OORYRKZC.json","view_paper":"https://pith.science/paper/PITCLEQ5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.11657&json=true","fetch_graph":"https://pith.science/api/pith-number/PITCLEQ5JPXAVCE257OORYRKZC/graph.json","fetch_events":"https://pith.science/api/pith-number/PITCLEQ5JPXAVCE257OORYRKZC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/PITCLEQ5JPXAVCE257OORYRKZC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/PITCLEQ5JPXAVCE257OORYRKZC/action/storage_attestation","attest_author":"https://pith.science/pith/PITCLEQ5JPXAVCE257OORYRKZC/action/author_attestation","sign_citation":"https://pith.science/pith/PITCLEQ5JPXAVCE257OORYRKZC/action/citation_signature","submit_replication":"https://pith.science/pith/PITCLEQ5JPXAVCE257OORYRKZC/action/replication_record"}},"created_at":"2026-07-05T09:55:14.914503+00:00","updated_at":"2026-07-05T09:55:14.914503+00:00"}