Reasoning for Complex Data through Self-Supervised Learning

Self-supervised learning deals with problems that have little or no available labeled data. Recent work has shown impressive results when underlying classes have significant semantic differences. We will discuss strategies to tackle to enable learning from unlabeled data even when samples from different classes are not prominently diverse. We approach the pr…