Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks

Code

• github.com/edenton/cc-gan
  OfficialIn papernone★ 0
• github.com/zll17/Neural_Topic_Models
  pytorch★ 435
• github.com/mojc/GAN_lesion_filling
  none★ 0
• github.com/Nirvan101/Image-Restoration-deep-learning
  none★ 0
• github.com/eriklindernoren/Keras-GAN
  pytorch★ 0
• github.com/eriklindernoren/PyTorch-GAN
  pytorch★ 0
• github.com/zcemycl/Matlab-GAN
  pytorch★ 0

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.

Tasks

Benchmark Results

Reproductions