  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 https://github.com/ljjcoder/Probabilistic-Contrastive-Learning.
