Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised
Visual Representation Learning
Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised
Visual Representation Learning
Contrastive learning methods for unsupervised visual representation learning have reached remarkable levels of transfer performance. We argue that the power of contrastive learning has yet to be fully unleashed, as current methods are trained only on instance-level pretext tasks, leading to representations that may be sub-optimal for downstream tasks requiring …