{"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/line-out-of-distribution-detection-by","title":"LINe: Out-of-Distribution Detection by Leveraging Important Neurons","arxiv_id":"2303.13995","date":"2023-03-24","proceeding":"CVPR 2023 1","authors":["Yong Hyun Ahn","Gyeong-Moon Park","Seong Tae Kim"],"abstract":"It is important to quantify the uncertainty of input samples, especially in mission-critical domains such as autonomous driving and healthcare, where failure predictions on out-of-distribution (OOD) data are likely to cause big problems. OOD detection problem fundamentally begins in that the model cannot express what it is not aware of. Post-hoc OOD detection approaches are widely explored because they do not require an additional re-training process which might degrade the model's performance and increase the training cost. In this study, from the perspective of neurons in the deep layer of the model representing high-level features, we introduce a new aspect for analyzing the difference in model outputs between in-distribution data and OOD data. We propose a novel method, Leveraging Important Neurons (LINe), for post-hoc Out of distribution detection. Shapley value-based pruning reduces the effects of noisy outputs by selecting only high-contribution neurons for predicting specific classes of input data and masking the rest. Activation clipping fixes all values above a certain threshold into the same value, allowing LINe to treat all the class-specific features equally and just consider the difference between the number of activated feature differences between in-distribution and OOD data. Comprehensive experiments verify the effectiveness of the proposed method by outperforming state-of-the-art post-hoc OOD detection methods on CIFAR-10, CIFAR-100, and ImageNet datasets.","url_abs":"https://arxiv.org/abs/2303.13995v1","url_pdf":"https://arxiv.org/pdf/2303.13995v1.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":"line-out-of-distribution-detection-by","repo_url":"https://github.com/yonghyun-ahn/line-out-of-distribution-detection-by-leveraging-important-neurons","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"out-of-distribution-detection","task_name":"Out-of-Distribution Detection"}],"methods":[{"method_slug":"pruning","method_name":"Pruning"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/out-of-distribution-detection-on-imagenet-1k-12","task":"Out-of-Distribution Detection","dataset":"ImageNet-1k vs Curated OODs (avg.)","model":"LINe (ResNet-50)","rank_in_archive_order":4,"of":16,"metrics":{"AUROC":"95.03","FPR95":"20.70"},"uses_additional_data":false},{"leaderboard":"/sota/out-of-distribution-detection-on-imagenet-1k-9","task":"Out-of-Distribution Detection","dataset":"ImageNet-1k vs Places","model":"LINe (ResNet50)","rank_in_archive_order":2,"of":25,"metrics":{"AUROC":"92.85","FPR95":"28.52"},"uses_additional_data":false},{"leaderboard":"/sota/out-of-distribution-detection-on-imagenet-1k-8","task":"Out-of-Distribution Detection","dataset":"ImageNet-1k vs SUN","model":"LINe (ResNet50)","rank_in_archive_order":2,"of":22,"metrics":{"AUROC":"95.26","FPR95":"19.48"},"uses_additional_data":false},{"leaderboard":"/sota/out-of-distribution-detection-on-imagenet-1k-10","task":"Out-of-Distribution Detection","dataset":"ImageNet-1k vs Textures","model":"LINe (ResNet-50)","rank_in_archive_order":12,"of":34,"metrics":{"AUROC":"94.44","FPR95":"22.54"},"uses_additional_data":false},{"leaderboard":"/sota/out-of-distribution-detection-on-imagenet-1k-3","task":"Out-of-Distribution Detection","dataset":"ImageNet-1k vs iNaturalist","model":"LINe (ResNet-50)","rank_in_archive_order":11,"of":28,"metrics":{"AUROC":"97.56","FPR95":"12.26"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2303.13995","atlas_url":"https://app.syntology.ai/?focus=2303.13995","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.13995"}},"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. 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