{"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/concurrent-misclassification-and-out-of","title":"Concurrent Misclassification and Out-of-Distribution Detection for Semantic Segmentation via Energy-Based Normalizing Flow","arxiv_id":"2305.09610","date":"2023-05-16","proceeding":null,"authors":["Denis Gudovskiy","Tomoyuki Okuno","Yohei Nakata"],"abstract":"Recent semantic segmentation models accurately classify test-time examples that are similar to a training dataset distribution. However, their discriminative closed-set approach is not robust in practical data setups with distributional shifts and out-of-distribution (OOD) classes. As a result, the predicted probabilities can be very imprecise when used as confidence scores at test time. To address this, we propose a generative model for concurrent in-distribution misclassification (IDM) and OOD detection that relies on a normalizing flow framework. The proposed flow-based detector with an energy-based inputs (FlowEneDet) can extend previously deployed segmentation models without their time-consuming retraining. Our FlowEneDet results in a low-complexity architecture with marginal increase in the memory footprint. FlowEneDet achieves promising results on Cityscapes, Cityscapes-C, FishyScapes and SegmentMeIfYouCan benchmarks in IDM/OOD detection when applied to pretrained DeepLabV3+ and SegFormer semantic segmentation models.","url_abs":"https://arxiv.org/abs/2305.09610v1","url_pdf":"https://arxiv.org/pdf/2305.09610v1.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":"concurrent-misclassification-and-out-of","repo_url":"https://github.com/gudovskiy/flowenedet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"out-of-distribution-detection","task_name":"Out-of-Distribution Detection"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"mix-ffn","method_name":"Mix-FFN"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"segformer","method_name":"SegFormer"},{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/anomaly-detection-on-fishyscapes-1","task":"Anomaly Detection","dataset":"Fishyscapes","model":"FlowEneDet","rank_in_archive_order":6,"of":8,"metrics":{"AP":"67.8","FPR95":"21.58"},"uses_additional_data":false},{"leaderboard":"/sota/anomaly-detection-on-fishyscapes-l-f","task":"Anomaly Detection","dataset":"Fishyscapes L&F","model":"FlowEneDet","rank_in_archive_order":5,"of":18,"metrics":{"AP":"50.15","FPR95":"5.20"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2305.09610","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}