title: Deep unsupervised learning through spatial contrasting
abstract: Convolutional networks have marked their place over the last few years as the
  best performing model for various visual tasks. They are, however, most suited
  for supervised learning from large amounts of labeled data. Previous attempts
  have been made to use unlabeled data to improve model performance by applying
  unsupervised techniques. These attempts require different architectures and training methods.
  In this work we present a novel approach for unsupervised training
  of Convolutional networks that is based on contrasting between spatial regions
  within images.  This criterion can be employed within conventional neural net-
  works and trained using standard techniques such as SGD and back-propagation,
  thus complementing supervised methods.
authors: Elad Hoffer Itay Hubara Nir Ailon
hIndex: Nir Ailon 28