  Unsupervised learning is just at a tipping point where it could really take
off. Among these approaches, contrastive learning has seen tremendous progress
and led to state-of-the-art performance. In this paper, we construct a novel
probabilistic graphical model that effectively incorporates the low rank
promoting prior into the framework of contrastive learning, referred to as
LORAC. In contrast to the existing conventional self-supervised approaches that
only considers independent learning, our hypothesis explicitly requires that
all the samples belonging to the same instance class lie on the same subspace
with small dimension. This heuristic poses particular joint learning
constraints to reduce the degree of freedom of the problem during the search of
the optimal network parameterization. Most importantly, we argue that the low
rank prior employed here is not unique, and many different priors can be
invoked in a similar probabilistic way, corresponding to different hypotheses
about underlying truth behind the contrastive features. Empirical evidences
show that the proposed algorithm clearly surpasses the state-of-the-art
approaches on multiple benchmarks, including image classification, object
detection, instance segmentation and keypoint detection.
