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.
@article{arxiv.1611.06430,
title = {Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks},
author = {Remi Denton and Sam Gross and Rob Fergus},
journal= {arXiv preprint arXiv:1611.06430},
year = {2024}
}