Deep Autoresolution Networks

Gabriel Pereyra, Christian Szegedy

Feb 18, 2016 (modified: Feb 18, 2016) ICLR 2016 workshop submission readers: everyone
  • Abstract: Despite the success of very deep convolutional neural networks, they currently operate at very low resolutions relative to modern cameras. Visual attention mechanisms address this by allowing models to access higher resolutions only when necessary. However, in certain cases, this higher resolution isn’t available. We show that autoresolution networks, which learn correspondences between lowresolution and high-resolution images, learn representations that improve lowresolution classification - without needing labeled high-resolution images.
  • Conflicts: google.com, usc.edu

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