A Visual Guide to Self-Labelling Images

In the past year, several methods for self-supervised learning of image representations have been proposed. A recent trend in the methods is using Contrastive Learning (SimCLR, PIRL, MoCo) which have given very promising results. However, as we had seen in our survey on self-supervised learning, there exist many other problem formulations for self-supervised learning. Combine clustering and representation learning together to learn both features and labels simultaneously. A paper Self-Labelling(SeLa) presented at ICLR 2020 by Asano et al. of the Visual Geometry Group(VGG), University of Oxford has a new take on this approach and achieved the state of the art results in various benchmarks.