Contrastive Learning with Nasty Noise
Fuente:
arXiv
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| Autor principal: | |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866912244438138880 |
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| author | Zhao, Ziruo |
| author_facet | Zhao, Ziruo |
| contents | Contrastive learning has emerged as a powerful paradigm for self-supervised representation learning. This work analyzes the theoretical limits of contrastive learning under nasty noise, where an adversary modifies or replaces training samples. Using PAC learning and VC-dimension analysis, lower and upper bounds on sample complexity in adversarial settings are established. Additionally, data-dependent sample complexity bounds based on the l2-distance function are derived. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_17872 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Contrastive Learning with Nasty Noise Zhao, Ziruo Machine Learning Artificial Intelligence Contrastive learning has emerged as a powerful paradigm for self-supervised representation learning. This work analyzes the theoretical limits of contrastive learning under nasty noise, where an adversary modifies or replaces training samples. Using PAC learning and VC-dimension analysis, lower and upper bounds on sample complexity in adversarial settings are established. Additionally, data-dependent sample complexity bounds based on the l2-distance function are derived. |
| title | Contrastive Learning with Nasty Noise |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2502.17872 |