{"type":"ScholarlyArticle","title":"Learning Calibrated Uncertainties for Domain Shift: A Distributionally Robust Learning Approach","authors":[{"name":"Haoxuan Wang","slug":"haoxuan-wang","given":"Haoxuan","family":"Wang","affiliation":"Department of Computer Science and Engineering and MoE Key Lab of Artificial Intelligence , Shanghai Jiao Tong University"},{"name":"Zhiding Yu","slug":"zhiding-yu","given":"Zhiding","family":"Yu","affiliation":""},{"name":"Yisong Yue","slug":"yisong-yue","given":"Yisong","family":"Yue","affiliation":"Department of Computing and Mathematical Sciences , California Institute of Technology"},{"name":"Animashree Anandkumar","slug":"animashree-anandkumar","given":"Animashree","family":"Anandkumar","affiliation":"Department of Computing and Mathematical Sciences , California Institute of Technology"},{"name":"Anqi Liu","slug":"anqi-liu","given":"Anqi","family":"Liu","affiliation":"Department of Computer Science , Johns Hopkins University"},{"name":"Junchi Yan","slug":"junchi-yan","given":"Junchi","family":"Yan","affiliation":"Department of Computer Science and Engineering and MoE Key Lab of Artificial Intelligence , Shanghai Jiao Tong University"}],"abstract":"We propose a framework for learning calibrated uncertainties under domain shifts, considering the case where the source (training) distribution differs from the target (test) distribution. We detect such domain shifts through the use of a differentiable density ratio estimator and train it together with the task network, composing an adjusted softmax predictive form that concerns the domain shift. In particular, the density ratio estimator yields a density ratio that reflects the closeness of a target (test) sample to the source (training) distribution. We employ it to adjust the uncertainty of prediction in the task network. This idea of using the density ratio is based on the distributionally robust learning (DRL) framework, which accounts for the domain shift through adversarial risk minimization. We demonstrate that our proposed method generates calibrated uncertainties that benefit many downstream tasks, such as unsupervised domain adaptation (UDA) and semi-supervised learning (SSL). On these tasks, methods like self-training and FixMatch use uncertainties to select confident pseudo-labels for re-training. Our experiments show that the introduction of DRL leads to significant improvements in cross-domain performance. We also demonstrate that the estimated density ratios show an agreement with the human selection frequencies, suggesting a positive correlation with a proxy of human perceived uncertainties.","doi":"10.24963/ijcai.2023/162","url":"https://ijcai.org/proceedings/2023/162","pdf":"https://ijcai.org/proceedings/2023/0162.pdf","markdown":"https://ijcai.org/proceedings/2023/162.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/162","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Computer Vision","track":"Main Track","number":162,"pages":{"first":1460,"last":1469},"keywords":["Machine Learning: ML: Multi-task and transfer learning","Computer Vision: CV: Machine learning for vision","Machine Learning: ML: Classification"],"videos":[],"references":[{"position":1,"raw":"[Antifakos et al., 2005] Stavros Antifakos, Nicky Kern, Bernt Schiele, and Adrian Schwaninger. 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