{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/back-to-the-basics-revisiting-out-of","title":"Back to the Basics: Revisiting Out-of-Distribution Detection Baselines","arxiv_id":"2207.03061","date":"2022-07-07","proceeding":null,"authors":["Johnson Kuan","Jonas Mueller"],"abstract":"We study simple methods for out-of-distribution (OOD) image detection that are compatible with any already trained classifier, relying on only its predictions or learned representations. Evaluating the OOD detection performance of various methods when utilized with ResNet-50 and Swin Transformer models, we find methods that solely consider the model's predictions can be easily outperformed by also considering the learned representations. Based on our analysis, we advocate for a dead-simple approach that has been neglected in other studies: simply flag as OOD images whose average distance to their K nearest neighbors is large (in the representation space of an image classifier trained on the in-distribution data).","url_abs":"https://arxiv.org/abs/2207.03061v1","url_pdf":"https://arxiv.org/pdf/2207.03061v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"back-to-the-basics-revisiting-out-of","repo_url":"https://github.com/cleanlab/cleanlab","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"back-to-the-basics-revisiting-out-of","repo_url":"https://github.com/cleanlab/ood-detection-benchmarks","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"mxnet","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"ood-detection","task_name":"Out of Distribution (OOD) Detection"},{"task_slug":"out-of-distribution-detection","task_name":"Out-of-Distribution Detection"}],"methods":[{"method_slug":"absolute-position-encodings","method_name":"Absolute Position Encodings"},{"method_slug":"adam","method_name":"Adam"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"label-smoothing","method_name":"Label Smoothing"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"position-wise-feed-forward-layer","method_name":"Position-Wise Feed-Forward Layer"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"stochastic-depth","method_name":"Stochastic Depth"},{"method_slug":"swin-transformer","method_name":"Swin Transformer"},{"method_slug":"transformer","method_name":"Transformer"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2207.03061","atlas_url":"https://app.syntology.ai/?focus=2207.03061","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.03061"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/cleanlab/cleanlab","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/cleanlab/ood-detection-benchmarks","reach":{"status":"ok","spdx":"Apache-2.0"}}],"summary":{"ran":6},"by_repo_kind":{"official":{"samples":6,"ran":6,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"137e31a502097297","entry":"find_dupes_in_data_dir","repo":"cleanlab/ood-detection-benchmarks","repo_kind":"official","path":"src/preprocess/remove_dupes.py","file_url":"https://github.com/cleanlab/ood-detection-benchmarks/blob/HEAD/src/preprocess/remove_dupes.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"137e31a502097297"}},{"code_sha256_prefix":"a8307d3e9159b810","entry":"find_dupes_in_data_split_dir","repo":"cleanlab/ood-detection-benchmarks","repo_kind":"official","path":"src/preprocess/remove_dupes.py","file_url":"https://github.com/cleanlab/ood-detection-benchmarks/blob/HEAD/src/preprocess/remove_dupes.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"a8307d3e9159b810"}},{"code_sha256_prefix":"4d49690d4932ba8c","entry":"fit_mahalanobis","repo":"cleanlab/ood-detection-benchmarks","repo_kind":"official","path":"src/experiments/OOD/mahalanobis.py","file_url":"https://github.com/cleanlab/ood-detection-benchmarks/blob/HEAD/src/experiments/OOD/mahalanobis.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"4d49690d4932ba8c"}},{"code_sha256_prefix":"cc65480057d34f19","entry":"fit_rmd","repo":"cleanlab/ood-detection-benchmarks","repo_kind":"official","path":"src/experiments/OOD/mahalanobis.py","file_url":"https://github.com/cleanlab/ood-detection-benchmarks/blob/HEAD/src/experiments/OOD/mahalanobis.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"cc65480057d34f19"}},{"code_sha256_prefix":"1e5ce5936af7b546","entry":"score_mahalanobis","repo":"cleanlab/ood-detection-benchmarks","repo_kind":"official","path":"src/experiments/OOD/mahalanobis.py","file_url":"https://github.com/cleanlab/ood-detection-benchmarks/blob/HEAD/src/experiments/OOD/mahalanobis.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"1e5ce5936af7b546"}},{"code_sha256_prefix":"f88aae44b2b0fdac","entry":"timefunc","repo":"cleanlab/ood-detection-benchmarks","repo_kind":"official","path":"src/utils/time_utils.py","file_url":"https://github.com/cleanlab/ood-detection-benchmarks/blob/HEAD/src/utils/time_utils.py","link_basis":"harvester_set","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"f88aae44b2b0fdac"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}