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Closely\nrelated to this is the task of out-of-distribution detection, where a network\nmust determine whether or not an input is outside of the set on which it is\nexpected to safely perform. To jointly address these issues, we propose a\nmethod of learning confidence estimates for neural networks that is simple to\nimplement and produces intuitively interpretable outputs. We demonstrate that\non the task of out-of-distribution detection, our technique surpasses recently\nproposed techniques which construct confidence based on the network's output\ndistribution, without requiring any additional labels or access to\nout-of-distribution examples. Additionally, we address the problem of\ncalibrating out-of-distribution detectors, where we demonstrate that\nmisclassified in-distribution examples can be used as a proxy for\nout-of-distribution examples.","url_abs":"http://arxiv.org/abs/1802.04865v1","url_pdf":"http://arxiv.org/pdf/1802.04865v1.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":"learning-confidence-for-out-of-distribution","repo_url":"https://github.com/uoguelph-mlrg/confidence_estimation","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"learning-confidence-for-out-of-distribution","repo_url":"https://github.com/Janus-Shiau/ood_confidence_tensorflow","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}},{"paper_slug":"learning-confidence-for-out-of-distribution","repo_url":"https://github.com/jun-cen/unified_open_set_recognition","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"learning-confidence-for-out-of-distribution","repo_url":"https://github.com/kkirchheim/pytorch-ood","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"learning-confidence-for-out-of-distribution","repo_url":"https://github.com/oliverzhang42/ood_medical_images","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"out-of-distribution-detection","task_name":"Out-of-Distribution Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.04865","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.04865"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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. 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