Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks

19 Nov 2016  ·  Emily 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. This task acts as a regularizer for standard supervised training of the discriminator. Using our approach we are able to directly train large VGG-style networks in a semi-supervised fashion. We evaluate on STL-10 and PASCAL datasets, where our approach obtains performance comparable or superior to existing methods.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Image Classification STL-10 CC-GAN² Percentage correct 77.8 # 70
Semi-Supervised Image Classification STL-10, 1000 Labels CC-GAN² Accuracy 77.80 # 8


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