一种用于自监督对比学习的增强感知理论
An Augmentation-Aware Theory for Self-Supervised Contrastive Learning
AI总结：
首次提出自监督对比学习的增强感知误差界，揭示数据增强引发的权衡对监督风险的影响，并通过像素级和表示级实验验证了理论结果。
摘要：
自监督对比学习已成为机器学习和计算机视觉中从无标签数据学习有意义表示的强大工具。同时，其实证成功也推动了许多理论研究来揭示其学习机制。然而，在现有理论研究中，数据增强的作用仍未被充分挖掘，特别是特定增强类型的影响。为填补这一空白，我们首次提出了自监督对比学习的增强感知误差界，表明监督风险不仅受无监督风险约束，还明确受数据增强引发的权衡所约束。随后，在一种新颖的语义标签假设下，我们讨论了某些增强方法如何影响该误差界。最后，我们进行了像素级和表示级实验，以验证我们提出的理论结果。
英文摘要：
Self-supervised contrastive learning has emerged as a powerful tool in machine learning and computer vision to learn meaningful representations from unlabeled data. Meanwhile, its empirical success has encouraged many theoretical studies to reveal the learning mechanisms. However, in the existing theoretical research, the role of data augmentation is still under-exploited, especially the effects of specific augmentation types. To fill in the blank, we for the first time propose an augmentation-aware error bound for self-supervised contrastive learning, showing that the supervised risk is bounded not only by the unsupervised risk, but also explicitly by a trade-off induced by data augmentation. Then, under a novel semantic label assumption, we discuss how certain augmentation methods affect the error bound. Lastly, we conduct both pixel- and representation-level experiments to verify our proposed theoretical results.
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。
