Contrastive Learning with Nasty Noise
- URL: http://arxiv.org/abs/2502.17872v1
- Date: Tue, 25 Feb 2025 05:55:15 GMT
- Title: Contrastive Learning with Nasty Noise
- Authors: Ziruo Zhao,
- Abstract summary: This work analyzes the theoretical limits of contrastive learning under nasty noise, where an adversary modifies or replaces training samples.<n>Data-dependent sample complexity bounds based on the l2-distance function are derived.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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