  This paper explores useful modifications of the recent development in
contrastive learning via novel probabilistic modeling. We derive a particular
form of contrastive loss named Joint Contrastive Learning (JCL). JCL implicitly
involves the simultaneous learning of an infinite number of query-key pairs,
which poses tighter constraints when searching for invariant features. We
derive an upper bound on this formulation that allows analytical solutions in
an end-to-end training manner. While JCL is practically effective in numerous
computer vision applications, we also theoretically unveil the certain
mechanisms that govern the behavior of JCL. We demonstrate that the proposed
formulation harbors an innate agency that strongly favors similarity within
each instance-specific class, and therefore remains advantageous when searching
for discriminative features among distinct instances. We evaluate these
proposals on multiple benchmarks, demonstrating considerable improvements over
existing algorithms. Code is publicly available at:
https://github.com/caiqi/Joint-Contrastive-Learning.
