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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.14424 |
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| _version_ | 1866912460895682560 |
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| author | Jiao, Yuling Ma, Wensen Sun, Defeng Wang, Hansheng Wang, Yang |
| author_facet | Jiao, Yuling Ma, Wensen Sun, Defeng Wang, Hansheng Wang, Yang |
| contents | In this paper, we propose a novel self-supervised transfer learning method called \underline{\textbf{D}}istribution \underline{\textbf{M}}atching (DM), which drives the representation distribution toward a predefined reference distribution while preserving augmentation invariance. DM results in a learned representation space that is intuitively structured and therefore easy to interpret.
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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_14424 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Distribution Matching for Self-Supervised Transfer Learning Jiao, Yuling Ma, Wensen Sun, Defeng Wang, Hansheng Wang, Yang Machine Learning Artificial Intelligence Methodology In this paper, we propose a novel self-supervised transfer learning method called \underline{\textbf{D}}istribution \underline{\textbf{M}}atching (DM), which drives the representation distribution toward a predefined reference distribution while preserving augmentation invariance. DM results in a learned representation space that is intuitively structured and therefore easy to interpret. 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. |
| title | Distribution Matching for Self-Supervised Transfer Learning |
| topic | Machine Learning Artificial Intelligence Methodology |
| url | https://arxiv.org/abs/2502.14424 |