{"type":"ScholarlyArticle","title":"Probabilistic Contrastive Learning for Domain Adaptation","authors":[{"name":"Junjie Li","slug":"junjie-li","given":"Junjie","family":"Li","affiliation":"Beijing University of Posts and Telecommunications; University of Science and Technology of China"},{"name":"Yixin Zhang","slug":"yixin-zhang","given":"Yixin","family":"Zhang","affiliation":"University of Science and Technology of China"},{"name":"Zilei Wang","slug":"zilei-wang","given":"Zilei","family":"Wang","affiliation":"University of Science and Technology of China"},{"name":"Saihui Hou","slug":"saihui-hou","given":"Saihui","family":"Hou","affiliation":"Beijing Normal University"},{"name":"Keyu Tu","slug":"keyu-tu","given":"Keyu","family":"Tu","affiliation":"University of Science and Technology of China"},{"name":"Man Zhang","slug":"man-zhang","given":"Man","family":"Zhang","affiliation":"Beijing University of Posts and Telecommunications"}],"abstract":"Contrastive learning has shown impressive success in enhancing feature discriminability for various visual tasks in a self-supervised manner, but the standard contrastive paradigm (features+l2 normalization) has limited benefits when applied in domain adaptation. We find that this is mainly because the class weights (weights of the final fully connected layer) are ignored in the domain adaptation optimization process, which makes it difficult for features to cluster around the corresponding class weights. To solve this problem, we propose the simple but powerful Probabilistic Contrastive Learning (PCL), which moves beyond the standard paradigm by removing l2 normalization and replacing the features with probabilities. PCL can guide the probability distribution towards a one-hot configuration, thus minimizing the discrepancy between features and class weights. We conduct extensive experiments to validate the effectiveness of PCL and observe consistent performance gains on five tasks, i.e., Unsupervised/Semi-Supervised Domain Adaptation (UDA/SSDA), Semi-Supervised Learning (SSL), UDA Detection and Semantic Segmentation. Notably, for UDA Semantic Segmentation on SYNTHIA, PCL surpasses the sophisticated CPSL-D by 2% in terms of mean IoU with a much lower training cost (PCL: 1*3090, 5 days v.s. CPSL-D: 4*V100, 11 days). Code is available at https://github.com/ljjcoder/Probabilistic-Contrastive-Learning.","doi":"10.24963/ijcai.2024/111","url":"https://ijcai.org/proceedings/2024/111","pdf":"https://ijcai.org/proceedings/2024/0111.pdf","markdown":"https://ijcai.org/proceedings/2024/111.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/111","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Computer Vision","track":"Main Track","number":111,"pages":{"first":1001,"last":1009},"keywords":["Computer Vision: CV: Transfer, low-shot, semi- and un- supervised learning","Computer Vision: CV: Recognition (object detection, categorization)"],"videos":[],"references":[{"position":1,"raw":"[Chen and He, 2021] Xinlei Chen and Kaiming He. 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