  Contrastive learning (CL) is a self-supervised training paradigm that allows
us to extract meaningful features without any label information. A typical CL
framework is divided into two phases, where it first tries to learn the
features from unlabelled data, and then uses those features to train a linear
classifier with the labeled data. While a fair amount of existing theoretical
works have analyzed how the unsupervised loss in the first phase can support
the supervised loss in the second phase, none has examined the connection
between the unsupervised loss and the robust supervised loss, which can shed
light on how to construct an effective unsupervised loss for the first phase of
CL. To fill this gap, our work develops rigorous theories to dissect and
identify which components in the unsupervised loss can help improve the robust
supervised loss and conduct proper experiments to verify our findings.
