Phillip Isola

Publications76
h-index30
Citations17,508
Highly Influential Citations3,406
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  • Publications
  • Influence
Image-to-Image Translation with Conditional Adversarial Networks
We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but… Expand
  • 5,915
  • 1264
“Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks”の学習報告
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Unpaired Image-to-Image Translation Using Cycle-Consistent Adversarial Networks
Image-to-image translation is a class of vision and graphics problems where the goal is to learn the mapping between an input image and an output image using a training set of aligned image pairs.… Expand
  • 2,060
  • 287
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
While it is nearly effortless for humans to quickly assess the perceptual similarity between two images, the underlying processes are thought to be quite complex. Despite this, the most widely used… Expand
  • 794
  • 216
Colorful Image Colorization
Given a grayscale photograph as input, this paper attacks the problem of hallucinating a plausible color version of the photograph. This problem is clearly underconstrained, so previous approaches… Expand
  • 1,111
  • 144
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
Domain adaptation is critical for success in new, unseen environments. Adversarial adaptation models applied in feature spaces discover domain invariant representations, but are difficult to… Expand
  • 796
  • 119
What Makes a Photograph Memorable?
When glancing at a magazine, or browsing the Internet, we are continuously exposed to photographs. Despite this overflow of visual information, humans are extremely good at remembering thousands of… Expand
  • 155
  • 31
Contrastive Multiview Coding
Humans view the world through many sensory channels, e.g., the long-wavelength light channel, viewed by the left eye, or the high-frequency vibrations channel, heard by the right ear. Each view is… Expand
  • 138
  • 28
What makes an image memorable
  • 154
  • 27
Split-Brain Autoencoders: Unsupervised Learning by Cross-Channel Prediction
We propose split-brain autoencoders, a straightforward modification of the traditional autoencoder architecture, for unsupervised representation learning. The method adds a split to the network,… Expand
  • 237
  • 25