The first paper presents a state-of-the-art approach for self-supervised learning of strong visual features based on contrastive learning. Random data augmentation is applied to images from the ImageNet dataset and a model is trained to match augmented and original images. The second paper revisits several self-supervised training techniques for visual representation learning and offers a nice overview over different approaches.
