对比学习与恶意噪声
Contrastive Learning with Nasty Noise
AI总结：
本文分析恶意噪声下对比学习的理论极限，利用PAC学习和VC维建立样本复杂性上下界，并推导基于l2距离的数据依赖界。
摘要：
对比学习已成为自监督表示学习的一种强大范式。本文分析了在恶意噪声下对比学习的理论极限，其中对手会修改或替换训练样本。利用PAC学习和VC维分析，建立了对抗设置下样本复杂性的下界和上界。此外，还推导了基于l2距离函数的数据依赖的样本复杂性界。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
