In this paper, we propose a novel self-supervised transfer learning method called Distribution Matching (DM), which drives the representation distribution toward a predefined reference distribution while preserving augmentation invariance. The design of DM results in a learned representation space that is intuitively structured and offers easily interpretable hyperparameters. Experimental results across multiple real-world datasets and evaluation metrics demonstrate that DM performs competitively on target classification tasks compared to existing self-supervised transfer learning methods. Additionally, we provide robust theoretical guarantees for DM, including a population theorem and an end-to-end sample theorem. The population theorem bridges the gap between the self-supervised learning task and target classification accuracy, while the sample theorem shows that, even with a limited number of samples from the target domain, DM can deliver exceptional classification performance, provided the unlabeled sample size is sufficiently large.

本文提出了一种新的自监督迁移学习方法——分布匹配（DM），旨在解决现有方法在目标分类任务中表现不佳的问题。DM通过将表示分布驱动向预定义参考分布，保持数据增强的不变性，实现在多个数据集上竞争性的分类成绩。研究表明，DM不仅提供了健全的理论保证，还能在样本量有限的情况下，依旧表现出色。 

自监督迁移学习的分布匹配