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:
this https URL

本文探讨在对比学习中通过新的概率模型进行的有效修改。通过推导一个特殊形式的对比损失函数，即联合对比学习（JCL），JCL 在搜索不变特征时具有更紧的约束条件。我们提出了对这种形式的上界的研究，这种上界允许端到端的训练方式中的解析性解决方案。尽管 JCL 在多个计算机视觉应用中实用，但我们也从理论上揭示了支配 JCL 行为的某些机制。我们证明了所提议的公式具有固有的机制，强烈倾向于在每个实例特定的类别内保持相似性，因此在搜索不同实例之间的辨别特征时仍具有优势。我们在多个基准上评估了这些提议，展示了对现有算法的显著改进。

具有无限可能性的联合对比学习

Joint Contrastive Learning with Infinite Possibilities

Enhancing feature transferability by matching marginal distributions has led
to improvements in domain adaptation, although this is at the expense of
feature discrimination. In particular, the ideal joint hypothesis error in the
target error upper bound, which was previously considered to be minute, has
been found to be significant, impairing its theoretical guarantee. In this
paper, we propose an alternative upper bound on the target error that
explicitly considers the joint error to render it more manageable. With the
theoretical analysis, we suggest a joint optimization framework that combines
the source and target domains. Further, we introduce Joint Contrastive Learning
(JCL) to find class-level discriminative features, which is essential for
minimizing the joint error. With a solid theoretical framework, JCL employs
contrastive loss to maximize the mutual information between a feature and its
label, which is equivalent to maximizing the Jensen-Shannon divergence between
conditional distributions. Experiments on two real-world datasets demonstrate
that JCL outperforms the state-of-the-art methods.

本文提出了一种将源域和目标域相结合的联合优化框架，并引入了一种称为 JCL 的新方法，它利用对比损失来最大化特征和标签之间的互信息，以最小化联合误差，从而有效提高了特征传递性和分类效果。