{"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/evaluating-bayesian-deep-learning-methods-for","title":"Evaluating Bayesian Deep Learning Methods for Semantic Segmentation","arxiv_id":"1811.12709","date":"2018-11-30","proceeding":null,"authors":["Jishnu Mukhoti","Yarin Gal"],"abstract":"Deep learning has been revolutionary for computer vision and semantic\nsegmentation in particular, with Bayesian Deep Learning (BDL) used to obtain\nuncertainty maps from deep models when predicting semantic classes. This\ninformation is critical when using semantic segmentation for autonomous driving\nfor example. Standard semantic segmentation systems have well-established\nevaluation metrics. However, with BDL's rising popularity in computer vision we\nrequire new metrics to evaluate whether a BDL method produces better\nuncertainty estimates than another method. In this work we propose three such\nmetrics to evaluate BDL models designed specifically for the task of semantic\nsegmentation. We modify DeepLab-v3+, one of the state-of-the-art deep neural\nnetworks, and create its Bayesian counterpart using MC dropout and Concrete\ndropout as inference techniques. We then compare and test these two inference\ntechniques on the well-known Cityscapes dataset using our suggested metrics.\nOur results provide new benchmarks for researchers to compare and evaluate\ntheir improved uncertainty quantification in pursuit of safer semantic\nsegmentation.","url_abs":"http://arxiv.org/abs/1811.12709v2","url_pdf":"http://arxiv.org/pdf/1811.12709v2.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":"evaluating-bayesian-deep-learning-methods-for","repo_url":"https://github.com/IntelLabs/AVUC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"uncertainty-quantification","task_name":"Uncertainty Quantification"}],"methods":[{"method_slug":"dropout","method_name":"Dropout"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/anomaly-detection-on-fishyscapes-1","task":"Anomaly Detection","dataset":"Fishyscapes","model":"Bayesian DeepLab","rank_in_archive_order":8,"of":8,"metrics":{"AP":"48.7","FPR95":"15.5"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.12709","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}