Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks

19 Nov 2016Emily Denton • Sam Gross • Rob Fergus

We introduce a simple semi-supervised learning approach for images based on in-painting using an adversarial loss. Images with random patches removed are presented to a generator whose task is to fill in the hole, based on the surrounding pixels. The in-painted images are then presented to a discriminator network that judges if they are real (unaltered training images) or not.

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Task Dataset Model Metric name Metric value Global rank Compare
Image Classification STL-10 CC-GAN² Percentage correct 77.79 # 1