  Recent breakthroughs in semi-supervised semantic segmentation have been
developed through contrastive learning. In prevalent pixel-wise contrastive
learning solutions, the model maps pixels to deterministic representations and
regularizes them in the latent space. However, there exist inaccurate
pseudo-labels which map the ambiguous representations of pixels to the wrong
classes due to the limited cognitive ability of the model. In this paper, we
define pixel-wise representations from a new perspective of probability theory
and propose a Probabilistic Representation Contrastive Learning (PRCL)
framework that improves representation quality by taking its probability into
consideration. Through modelling the mapping from pixels to representations as
the probability via multivariate Gaussian distributions, we can tune the
contribution of the ambiguous representations to tolerate the risk of
inaccurate pseudo-labels. Furthermore, we define prototypes in the form of
distributions, which indicates the confidence of a class, while the point
prototype cannot. Moreover, we propose to regularize the distribution variance
to enhance the reliability of representations. Taking advantage of these
benefits, high-quality feature representations can be derived in the latent
space, thereby the performance of semantic segmentation can be further
improved. We conduct sufficient experiment to evaluate PRCL on Pascal VOC and
CityScapes to demonstrate its superiority. The code is available at
https://github.com/Haoyu-Xie/PRCL.
