Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks

Emily Denton, Sam Gross, Rob Fergus

Abstract: 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.

TL;DR: Training GANs to in-paint images produces feature representations that yield leading results on various benchmarks.

Conflicts: nyu.edu, fb.com

Keywords: Deep learning, Semi-Supervised Learning, Computer vision

Community Implementations: [ 7 code implementations](https://www.catalyzex.com/paper/arxiv:1611.06430/code)