Contrastive learning, multi-view redundancy, and linear models
Data Science Institute, Columbia University（哥伦比亚大学数据科学研究所）；Microsoft Research（微软研究院）；Department of Computer Science, Columbia University（哥伦比亚大学计算机科学系）
英文摘要：
Self-supervised learning is an empirically successful approach to unsupervised learning based on creating artificial supervised learning problems. A popular self-supervised approach to representation learning is contrastive learning, which leverages naturally occurring pairs of similar and dissimilar data points, or multiple views of the same data. This work provides a theoretical analysis of contrastive learning in the multi-view setting, where two views of each datum are available. The main result is that linear functions of the learned representations are nearly optimal on downstream prediction tasks whenever the two views provide redundant information about the label.
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。
