CRITICAL ANALYSIS OF SELF-SUPERVISION
Presented by
Maral Rasoolijaberi
Introduction
Previous Work
In recent literature, several papers addressed unsupervised learning methods and learning from a single sample.
A BiGAN [Donahue et al., 2017], or Bidirectional GAN, is basically a generative adversarial network plus an encoder. The generator maps latent samples to generated data and the encoder performs as the opposite of the generator. After training BiGAN, the encoder has learned to generate a rich image representation. In RotNet method [Gidaris et al., 2018], images are rotated and the CNN learns to figure out the direction. DeepCluster [Caron et al., 2018] alternates k-means clustering to learn stable feature representations under several image transformations.