An overview of current trends for feature learning in the unsupervised way: regress to random targets for manifold learning, exploit causality to characterize visual features, and in reinforcement learning, augment the objective with auxiliary control tasks and pre-train by self-play. There is so much to learn from unlabeled data and it seems that we have only skimmed the surface of it by only using labels.

Source: In search of the missing signals