论文标题
同态自学学习
Homomorphic Self-Supervised Learning
论文作者
论文摘要
在这项工作中，我们观察到，当通过模棱两可的表示镜头看到时，许多现有的自我监督学习算法既可以统一和普遍。具体来说，我们介绍了一个通用框架，我们称为同态自我监督学习，从理论上讲，它如何包含输入仪的使用提供了增强同构特征提取器。我们通过实验验证了该理论的简单增强，证明了删除表示结构时该框架如何失败，并进一步探索了该框架的参数与传统基于基于增强的自我监督学习的参数如何相关。最后，我们讨论了这种对自学学习的新观点所带来的潜在好处。
In this work, we observe that many existing self-supervised learning algorithms can be both unified and generalized when seen through the lens of equivariant representations. Specifically, we introduce a general framework we call Homomorphic Self-Supervised Learning, and theoretically show how it may subsume the use of input-augmentations provided an augmentation-homomorphic feature extractor. We validate this theory experimentally for simple augmentations, demonstrate how the framework fails when representational structure is removed, and further empirically explore how the parameters of this framework relate to those of traditional augmentation-based self-supervised learning. We conclude with a discussion of the potential benefits afforded by this new perspective on self-supervised learning.
