Deep unsupervised learning through spatial contrasting

Elad Hoffer, Itay Hubara, Nir Ailon

Oct 19, 2016 (modified: Jan 10, 2017) ICLR 2017 conference submission readers: everyone
  • 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.
  • Keywords: Unsupervised Learning, Deep learning, Computer vision
  • Conflicts: technion.ac.il, intel.com

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