Computer Science > Computer Vision and Pattern Recognition
[Submitted on 11 Nov 2021 (v1), last revised 8 Jun 2024 (this version, v6)]
Title:Probabilistic Contrastive Learning for Domain Adaptation
View PDF HTML (experimental)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+$\ell_{2}$ 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 \emph{simple but powerful} Probabilistic Contrastive Learning (PCL), which moves beyond the standard paradigm by removing $\ell_{2}$ 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 this https URL.
Submission history
From: Junjie Li [view email][v1] Thu, 11 Nov 2021 02:08:07 UTC (513 KB)
[v2] Mon, 29 Nov 2021 08:15:13 UTC (1,352 KB)
[v3] Tue, 8 Mar 2022 10:00:37 UTC (8,191 KB)
[v4] Mon, 21 Nov 2022 12:57:26 UTC (1,988 KB)
[v5] Sun, 16 Jul 2023 14:07:07 UTC (2,008 KB)
[v6] Sat, 8 Jun 2024 09:03:59 UTC (2,551 KB)
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