{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:BUMLJQ7WX5OKKU6FLHQPMI4GMR","short_pith_number":"pith:BUMLJQ7W","schema_version":"1.0","canonical_sha256":"0d18b4c3f6bf5ca553c559e0f623866453af2c877ca03b6ed7e62aabfe5be32c","source":{"kind":"arxiv","id":"1904.00760","version":1},"attestation_state":"computed","paper":{"title":"Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Matthias Bethge, Wieland Brendel","submitted_at":"2019-03-20T16:37:17Z","abstract_excerpt":"Deep Neural Networks (DNNs) excel on many complex perceptual tasks but it has proven notoriously difficult to understand how they reach their decisions. We here introduce a high-performance DNN architecture on ImageNet whose decisions are considerably easier to explain. Our model, a simple variant of the ResNet-50 architecture called BagNet, classifies an image based on the occurrences of small local image features without taking into account their spatial ordering. This strategy is closely related to the bag-of-feature (BoF) models popular before the onset of deep learning and reaches a surpr"},"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":"1904.00760","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-03-20T16:37:17Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"2248255d56b7977005ab2d704a79e775a9843044e0b0cb6e6a36b1795d2074df","abstract_canon_sha256":"a7ebe51468110905c6f1029e56489edbd7fe87bd55b43805d5d2b85b68d6aca9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:49:45.552356Z","signature_b64":"NJQejdV4NS/ywTXbO/M7FYocpaX0JM1u/Gc/j6ka8lg9Mrl6fQbcFcu8YH3iu8tAls50dqJeFD3/ehSdk7i7DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0d18b4c3f6bf5ca553c559e0f623866453af2c877ca03b6ed7e62aabfe5be32c","last_reissued_at":"2026-05-17T23:49:45.551789Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:49:45.551789Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Approximating CNNs with Bag-of-local-Features models works surprisingly well on ImageNet","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.CV","authors_text":"Matthias Bethge, Wieland Brendel","submitted_at":"2019-03-20T16:37:17Z","abstract_excerpt":"Deep Neural Networks (DNNs) excel on many complex perceptual tasks but it has proven notoriously difficult to understand how they reach their decisions. We here introduce a high-performance DNN architecture on ImageNet whose decisions are considerably easier to explain. Our model, a simple variant of the ResNet-50 architecture called BagNet, classifies an image based on the occurrences of small local image features without taking into account their spatial ordering. This strategy is closely related to the bag-of-feature (BoF) models popular before the onset of deep learning and reaches a surpr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.00760","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":""},"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":"1904.00760","created_at":"2026-05-17T23:49:45.551870+00:00"},{"alias_kind":"arxiv_version","alias_value":"1904.00760v1","created_at":"2026-05-17T23:49:45.551870+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.00760","created_at":"2026-05-17T23:49:45.551870+00:00"},{"alias_kind":"pith_short_12","alias_value":"BUMLJQ7WX5OK","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_16","alias_value":"BUMLJQ7WX5OKKU6F","created_at":"2026-05-18T12:33:12.712433+00:00"},{"alias_kind":"pith_short_8","alias_value":"BUMLJQ7W","created_at":"2026-05-18T12:33:12.712433+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":4,"internal_anchor_count":3,"sample":[{"citing_arxiv_id":"2607.06839","citing_title":"LEMUR 2: Unlocking Neural Network Diversity for AI","ref_index":3,"is_internal_anchor":true},{"citing_arxiv_id":"1907.08514","citing_title":"Predicting Visual Memory Schemas with Variational Autoencoders","ref_index":3,"is_internal_anchor":true},{"citing_arxiv_id":"2605.20771","citing_title":"Cumulative Meta-Learning from Active Learning Queries for Robustness to Spurious Correlations","ref_index":61,"is_internal_anchor":true},{"citing_arxiv_id":"2604.25065","citing_title":"ShapeY: A Principled Framework for Measuring Shape Recognition Capacity via Nearest-Neighbor Matching","ref_index":8,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BUMLJQ7WX5OKKU6FLHQPMI4GMR","json":"https://pith.science/pith/BUMLJQ7WX5OKKU6FLHQPMI4GMR.json","graph_json":"https://pith.science/api/pith-number/BUMLJQ7WX5OKKU6FLHQPMI4GMR/graph.json","events_json":"https://pith.science/api/pith-number/BUMLJQ7WX5OKKU6FLHQPMI4GMR/events.json","paper":"https://pith.science/paper/BUMLJQ7W"},"agent_actions":{"view_html":"https://pith.science/pith/BUMLJQ7WX5OKKU6FLHQPMI4GMR","download_json":"https://pith.science/pith/BUMLJQ7WX5OKKU6FLHQPMI4GMR.json","view_paper":"https://pith.science/paper/BUMLJQ7W","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1904.00760&json=true","fetch_graph":"https://pith.science/api/pith-number/BUMLJQ7WX5OKKU6FLHQPMI4GMR/graph.json","fetch_events":"https://pith.science/api/pith-number/BUMLJQ7WX5OKKU6FLHQPMI4GMR/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BUMLJQ7WX5OKKU6FLHQPMI4GMR/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BUMLJQ7WX5OKKU6FLHQPMI4GMR/action/storage_attestation","attest_author":"https://pith.science/pith/BUMLJQ7WX5OKKU6FLHQPMI4GMR/action/author_attestation","sign_citation":"https://pith.science/pith/BUMLJQ7WX5OKKU6FLHQPMI4GMR/action/citation_signature","submit_replication":"https://pith.science/pith/BUMLJQ7WX5OKKU6FLHQPMI4GMR/action/replication_record"}},"created_at":"2026-05-17T23:49:45.551870+00:00","updated_at":"2026-05-17T23:49:45.551870+00:00"}