Understanding Difficult-to-learn Examples in Contrastive Learning: A Theoretical Framework for Spectral Contrastive Learning Article Swipe
Yi-Ge Zhang
,
Jingyi Cui
,
Qunqing Li
,
Yisen Wang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2501.01317
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2501.01317
Unsupervised contrastive learning has shown significant performance improvements in recent years, often approaching or even rivaling supervised learning in various tasks. However, its learning mechanism is fundamentally different from that of supervised learning. Previous works have shown that difficult-to-learn examples (well-recognized in supervised learning as examples around the decision boundary), which are essential in supervised learning, contribute minimally in unsupervised settings. In this paper, perhaps surprisingly, we find that the direct removal of difficult-to-learn examples, although reduces the sample size, can boost the downstream classification performance of contrastive learning. To uncover the reasons behind this, we develop a theoretical framework modeling the similarity between different pairs of samples. Guided by this theoretical framework, we conduct a thorough theoretical analysis revealing that the presence of difficult-to-learn examples negatively affects the generalization of contrastive learning. Furthermore, we demonstrate that the removal of these examples, and techniques such as margin tuning and temperature scaling can enhance its generalization bounds, thereby improving performance. Empirically, we propose a simple and efficient mechanism for selecting difficult-to-learn examples and validate the effectiveness of the aforementioned methods, which substantiates the reliability of our proposed theoretical framework.
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- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2501.01317
- https://arxiv.org/pdf/2501.01317
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
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https://openalex.org/W4406033380Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2501.01317Digital Object Identifier
- Title
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Understanding Difficult-to-learn Examples in Contrastive Learning: A Theoretical Framework for Spectral Contrastive LearningWork title
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preprintOpenAlex work type
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enPrimary language
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2025Year of publication
- Publication date
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2025-01-02Full publication date if available
- Authors
-
Yi-Ge Zhang, Jingyi Cui, Qunqing Li, Yisen WangList of authors in order
- Landing page
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https://arxiv.org/abs/2501.01317Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2501.01317Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
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https://arxiv.org/pdf/2501.01317Direct OA link when available
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Contrastive analysis, Computer science, Linguistics, Cognitive science, Psychology, PhilosophyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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