Semi Supervised Learning with Context Conditional Generative Adversarial Networks
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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