{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:BMVT4YFRQBOSTMECK6VPHWGC6Y","short_pith_number":"pith:BMVT4YFR","canonical_record":{"source":{"id":"2112.08459","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-12-15T20:15:01Z","cross_cats_sorted":[],"title_canon_sha256":"d3f6a8263342c1e8dc32ca4a69264f90c241f442b7e74fe5c2ed42ea54050bfb","abstract_canon_sha256":"9f5cc26ed409e0b85f4258c0217d8c1b8b115025746d21e5a7db53ac8cf456e1"},"schema_version":"1.0"},"canonical_sha256":"0b2b3e60b1805d29b08257aaf3d8c2f62407ac84541a794cb93f6cd02f890808","source":{"kind":"arxiv","id":"2112.08459","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2112.08459","created_at":"2026-07-05T03:41:50Z"},{"alias_kind":"arxiv_version","alias_value":"2112.08459v2","created_at":"2026-07-05T03:41:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.08459","created_at":"2026-07-05T03:41:50Z"},{"alias_kind":"pith_short_12","alias_value":"BMVT4YFRQBOS","created_at":"2026-07-05T03:41:50Z"},{"alias_kind":"pith_short_16","alias_value":"BMVT4YFRQBOSTMEC","created_at":"2026-07-05T03:41:50Z"},{"alias_kind":"pith_short_8","alias_value":"BMVT4YFR","created_at":"2026-07-05T03:41:50Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:BMVT4YFRQBOSTMECK6VPHWGC6Y","target":"record","payload":{"canonical_record":{"source":{"id":"2112.08459","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-12-15T20:15:01Z","cross_cats_sorted":[],"title_canon_sha256":"d3f6a8263342c1e8dc32ca4a69264f90c241f442b7e74fe5c2ed42ea54050bfb","abstract_canon_sha256":"9f5cc26ed409e0b85f4258c0217d8c1b8b115025746d21e5a7db53ac8cf456e1"},"schema_version":"1.0"},"canonical_sha256":"0b2b3e60b1805d29b08257aaf3d8c2f62407ac84541a794cb93f6cd02f890808","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:41:50.167393Z","signature_b64":"4Is9oqGEU10stjTtcEY98grrKSwi0rDShAUBpItpVhXhJf/+/WnnBVpXwcDcAG9ZOo0tU5nrnG8K0XuWBC18Cw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0b2b3e60b1805d29b08257aaf3d8c2f62407ac84541a794cb93f6cd02f890808","last_reissued_at":"2026-07-05T03:41:50.166926Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:41:50.166926Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2112.08459","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:41:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BM0xsDvTGa2jcg2vVo/0e9UXFjJdEb5dUSzp+gufcqqkLo1ikc44zaOC1+Yz1enDmVgyTyeU/14djACINsNUBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-17T06:22:30.923947Z"},"content_sha256":"4de5b386bb50c4c5d92869bd4a9bba469163a4aa13de9e0d09bbaea32e3206cd","schema_version":"1.0","event_id":"sha256:4de5b386bb50c4c5d92869bd4a9bba469163a4aa13de9e0d09bbaea32e3206cd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:BMVT4YFRQBOSTMECK6VPHWGC6Y","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Rethinking Nearest Neighbors for Visual Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Bor-Chun Chen, Claire Cardie, Menglin Jia, Serge Belongie, Ser-Nam Lim, Zuxuan Wu","submitted_at":"2021-12-15T20:15:01Z","abstract_excerpt":"Neural network classifiers have become the de-facto choice for current \"pre-train then fine-tune\" paradigms of visual classification. In this paper, we investigate k-Nearest-Neighbor (k-NN) classifiers, a classical model-free learning method from the pre-deep learning era, as an augmentation to modern neural network based approaches. As a lazy learning method, k-NN simply aggregates the distance between the test image and top-k neighbors in a training set. We adopt k-NN with pre-trained visual representations produced by either supervised or self-supervised methods in two steps: (1) Leverage k"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.08459","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/2112.08459/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:41:50Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dDswQGwA1T4wsi9QW06eQwXcwb8Vsc8AjZgWMKc+qYeE3MG+jmCuCYWATGhQ8NSfBS2Up1QeImLEAjODH9ypBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-17T06:22:30.924337Z"},"content_sha256":"cc9230439506e4159b7c05e592c5618611e54d95e413995409a2c060519cd40f","schema_version":"1.0","event_id":"sha256:cc9230439506e4159b7c05e592c5618611e54d95e413995409a2c060519cd40f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BMVT4YFRQBOSTMECK6VPHWGC6Y/bundle.json","state_url":"https://pith.science/pith/BMVT4YFRQBOSTMECK6VPHWGC6Y/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BMVT4YFRQBOSTMECK6VPHWGC6Y/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-17T06:22:30Z","links":{"resolver":"https://pith.science/pith/BMVT4YFRQBOSTMECK6VPHWGC6Y","bundle":"https://pith.science/pith/BMVT4YFRQBOSTMECK6VPHWGC6Y/bundle.json","state":"https://pith.science/pith/BMVT4YFRQBOSTMECK6VPHWGC6Y/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BMVT4YFRQBOSTMECK6VPHWGC6Y/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:BMVT4YFRQBOSTMECK6VPHWGC6Y","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"9f5cc26ed409e0b85f4258c0217d8c1b8b115025746d21e5a7db53ac8cf456e1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-12-15T20:15:01Z","title_canon_sha256":"d3f6a8263342c1e8dc32ca4a69264f90c241f442b7e74fe5c2ed42ea54050bfb"},"schema_version":"1.0","source":{"id":"2112.08459","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2112.08459","created_at":"2026-07-05T03:41:50Z"},{"alias_kind":"arxiv_version","alias_value":"2112.08459v2","created_at":"2026-07-05T03:41:50Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2112.08459","created_at":"2026-07-05T03:41:50Z"},{"alias_kind":"pith_short_12","alias_value":"BMVT4YFRQBOS","created_at":"2026-07-05T03:41:50Z"},{"alias_kind":"pith_short_16","alias_value":"BMVT4YFRQBOSTMEC","created_at":"2026-07-05T03:41:50Z"},{"alias_kind":"pith_short_8","alias_value":"BMVT4YFR","created_at":"2026-07-05T03:41:50Z"}],"graph_snapshots":[{"event_id":"sha256:cc9230439506e4159b7c05e592c5618611e54d95e413995409a2c060519cd40f","target":"graph","created_at":"2026-07-05T03:41:50Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2112.08459/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Neural network classifiers have become the de-facto choice for current \"pre-train then fine-tune\" paradigms of visual classification. In this paper, we investigate k-Nearest-Neighbor (k-NN) classifiers, a classical model-free learning method from the pre-deep learning era, as an augmentation to modern neural network based approaches. As a lazy learning method, k-NN simply aggregates the distance between the test image and top-k neighbors in a training set. We adopt k-NN with pre-trained visual representations produced by either supervised or self-supervised methods in two steps: (1) Leverage k","authors_text":"Bor-Chun Chen, Claire Cardie, Menglin Jia, Serge Belongie, Ser-Nam Lim, Zuxuan Wu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-12-15T20:15:01Z","title":"Rethinking Nearest Neighbors for Visual Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2112.08459","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:4de5b386bb50c4c5d92869bd4a9bba469163a4aa13de9e0d09bbaea32e3206cd","target":"record","created_at":"2026-07-05T03:41:50Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"9f5cc26ed409e0b85f4258c0217d8c1b8b115025746d21e5a7db53ac8cf456e1","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2021-12-15T20:15:01Z","title_canon_sha256":"d3f6a8263342c1e8dc32ca4a69264f90c241f442b7e74fe5c2ed42ea54050bfb"},"schema_version":"1.0","source":{"id":"2112.08459","kind":"arxiv","version":2}},"canonical_sha256":"0b2b3e60b1805d29b08257aaf3d8c2f62407ac84541a794cb93f6cd02f890808","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0b2b3e60b1805d29b08257aaf3d8c2f62407ac84541a794cb93f6cd02f890808","first_computed_at":"2026-07-05T03:41:50.166926Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:41:50.166926Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4Is9oqGEU10stjTtcEY98grrKSwi0rDShAUBpItpVhXhJf/+/WnnBVpXwcDcAG9ZOo0tU5nrnG8K0XuWBC18Cw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:41:50.167393Z","signed_message":"canonical_sha256_bytes"},"source_id":"2112.08459","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4de5b386bb50c4c5d92869bd4a9bba469163a4aa13de9e0d09bbaea32e3206cd","sha256:cc9230439506e4159b7c05e592c5618611e54d95e413995409a2c060519cd40f"],"state_sha256":"65aef3a926995a852ceb66b0f8cee53ddef64e1b16efe2a8b8ee26e9d6f18baa"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Ac6Fq0VPSDKin2Mz787vW728bdWpzerOm5yA7HiX0Xrhuyb21zz+6nHvw9EepfCQb9olrAEMUNv0NjI+IV6nCA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-17T06:22:30.926375Z","bundle_sha256":"840065fd6904a1b58b378808fd509c8b133c7ee114e136f144bc1412469b7638"}}