Contrastive learning
Contrastive Learning (CL) tries to predict the relationship between a pair of input samples. The goal of CL is to learn an embedding space where pairs of similar samples are pulled close together and dissimilar samples are pushed far apart. Inputs to train CL models are in the form of pairs of data points. CL can be used in both supervised and unsupervised settings.
When used in an unsupervised setting, it can be a very powerful self-supervised learning approach. Similar pairs are found from existing data in a self-supervised manner, and dissimilar pairs are found from pairs of similar pairs of data. The model learns to predict if a pair of data points are similar or different.
A taxonomy of CL can be derived by considering the techniques used to generate contrastive examples. Before we do that, we will take a brief detour to explore the various training objectives that are popular in CL.
