Distribution Matching for Self-Supervised Transfer Learning

• URL: http://arxiv.org/abs/2502.14424v1
• Date: Thu, 20 Feb 2025 10:20:56 GMT
• Title: Distribution Matching for Self-Supervised Transfer Learning
• Authors: Yuling Jiao, Wensen Ma, Defeng Sun, Hansheng Wang, Yang Wang,
• Abstract summary: We propose a novel self-supervised transfer learning method called Distribution Matching. We show that DM performs competitively on target classification tasks compared to existing self-supervised transfer learning methods. We provide robust theoretical guarantees for DM, including a population theorem and an end-to-end sample theorem.
• Score: 9.549045683389085
• License:
• Abstract: 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.

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