{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:4RERM4EE4TA5VWZU6XEBE6DWM7","short_pith_number":"pith:4RERM4EE","schema_version":"1.0","canonical_sha256":"e449167084e4c1dadb34f5c812787667d7e30a50bc1739d6f2c70f2259b39b1a","source":{"kind":"arxiv","id":"2502.00663","version":1},"attestation_state":"computed","paper":{"title":"Enhanced Convolutional Neural Networks for Improved Image Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Shuhan Yu, Wenxi Xu, Xiaoran Yang","submitted_at":"2025-02-02T04:32:25Z","abstract_excerpt":"Image classification is a fundamental task in computer vision with diverse applications, ranging from autonomous systems to medical imaging. The CIFAR-10 dataset is a widely used benchmark to evaluate the performance of classification models on small-scale, multi-class datasets. Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art results; however, they often suffer from overfitting and suboptimal feature representation when applied to challenging datasets like CIFAR-10. In this paper, we propose an enhanced CNN architecture that integrates deeper convolutional blocks, batch"},"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":"2502.00663","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-02-02T04:32:25Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"2bc2105bd8ea51d13ce9e17b2091e904456d8201f812f3c2bd28b77913767c77","abstract_canon_sha256":"197d6bd5e6df488d70fc37b220ff42d1fc7b7741726c802184353796df6d069d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:08:36.801536Z","signature_b64":"ZDS6nZmj84QfQOVdiZXbndfR/ugqvsPE8/q1QP+zznzDZnziUDKALpNHuO8S22/djVNxJ8s0wTsDQuIooEzhDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e449167084e4c1dadb34f5c812787667d7e30a50bc1739d6f2c70f2259b39b1a","last_reissued_at":"2026-07-05T10:08:36.801103Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:08:36.801103Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Enhanced Convolutional Neural Networks for Improved Image Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Shuhan Yu, Wenxi Xu, Xiaoran Yang","submitted_at":"2025-02-02T04:32:25Z","abstract_excerpt":"Image classification is a fundamental task in computer vision with diverse applications, ranging from autonomous systems to medical imaging. The CIFAR-10 dataset is a widely used benchmark to evaluate the performance of classification models on small-scale, multi-class datasets. Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art results; however, they often suffer from overfitting and suboptimal feature representation when applied to challenging datasets like CIFAR-10. In this paper, we propose an enhanced CNN architecture that integrates deeper convolutional blocks, batch"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.00663","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/2502.00663/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":"2502.00663","created_at":"2026-07-05T10:08:36.801160+00:00"},{"alias_kind":"arxiv_version","alias_value":"2502.00663v1","created_at":"2026-07-05T10:08:36.801160+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.00663","created_at":"2026-07-05T10:08:36.801160+00:00"},{"alias_kind":"pith_short_12","alias_value":"4RERM4EE4TA5","created_at":"2026-07-05T10:08:36.801160+00:00"},{"alias_kind":"pith_short_16","alias_value":"4RERM4EE4TA5VWZU","created_at":"2026-07-05T10:08:36.801160+00:00"},{"alias_kind":"pith_short_8","alias_value":"4RERM4EE","created_at":"2026-07-05T10:08:36.801160+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/4RERM4EE4TA5VWZU6XEBE6DWM7","json":"https://pith.science/pith/4RERM4EE4TA5VWZU6XEBE6DWM7.json","graph_json":"https://pith.science/api/pith-number/4RERM4EE4TA5VWZU6XEBE6DWM7/graph.json","events_json":"https://pith.science/api/pith-number/4RERM4EE4TA5VWZU6XEBE6DWM7/events.json","paper":"https://pith.science/paper/4RERM4EE"},"agent_actions":{"view_html":"https://pith.science/pith/4RERM4EE4TA5VWZU6XEBE6DWM7","download_json":"https://pith.science/pith/4RERM4EE4TA5VWZU6XEBE6DWM7.json","view_paper":"https://pith.science/paper/4RERM4EE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2502.00663&json=true","fetch_graph":"https://pith.science/api/pith-number/4RERM4EE4TA5VWZU6XEBE6DWM7/graph.json","fetch_events":"https://pith.science/api/pith-number/4RERM4EE4TA5VWZU6XEBE6DWM7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4RERM4EE4TA5VWZU6XEBE6DWM7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4RERM4EE4TA5VWZU6XEBE6DWM7/action/storage_attestation","attest_author":"https://pith.science/pith/4RERM4EE4TA5VWZU6XEBE6DWM7/action/author_attestation","sign_citation":"https://pith.science/pith/4RERM4EE4TA5VWZU6XEBE6DWM7/action/citation_signature","submit_replication":"https://pith.science/pith/4RERM4EE4TA5VWZU6XEBE6DWM7/action/replication_record"}},"created_at":"2026-07-05T10:08:36.801160+00:00","updated_at":"2026-07-05T10:08:36.801160+00:00"}