Self-supervised learning with an information maximization criterion

Self-supervised learning provides a solution to learn effective representations from large amounts of data without performing data labeling, which is often expensive in terms of time, effort, and cost.The main problem with the self-supervised learning approach, in general, is collapse, i.e., obtaining identical representations for all inputs while matching different representations generated from the same input. In this thesis, we argue that information maximization among latent representations of different versions of the same input naturally prevents collapse. To this end, we propose a novel self-supervised learning method, CorInfoMax, based on maximizing the second-order statistics-based measure of mutual information that reflects the degree of correlation between the latent representation arguments. Maximizing this correlative information measure between alternative latent representations of the same input serves two main purposes: (1) it avoids the collapse problem by generating feature vectors with non-degenerate covariances; (2) it increases the linear dependence between alternative representations, ensuring that they are related to each other. The proposed information maximization objective is simplified to an objective function based on Euclidean distance regularized by the log-determinant of the feature covariance matrix. Due to the regularization term acting as a natural barrier against feature space degeneracy, CorInfoMax also prevents dimensional collapse by enforcing representations to span across the entire feature space. Empirical experiments show that CorInfoMax achieves better or competitive performance results over state-of-the-art self-supervised learning methods across different tasks and datasets.