Summary of "Conditional Generative Adversarial Nets" Paper

Conditional Generative Adversarial Nets.md

Conditional Generative Adversarial Nets

Introduction

• Conditional version of Generative Adversarial Nets (GAN) where both generator and discriminator are conditioned on some data y (class label or data from some other modality).
• Link to the paper

Architecture

• Feed y into both the generator and discriminator as additional input layers such that y and input are combined in a joint hidden representation.

Experiment

Unimodal Setting

• Conditioning MNIST images on class labels.
• z (random noise) and y mapped to hidden layers with ReLu with layer sizes of 200 and 1000 respectively and are combined to obtain ReLu layer of dimensionality 1200.
• Discriminator maps x (input) and y to maxout layers and the joint maxout layer is fed to sigmoid layer.
• Results do not outperform the state-of-the-art results but do provide a proof-of-the-concept.

Multimodal Setting

• Map images (from Flickr) to labels (or user tags) to obtain the one-to-many mapping.
• Extract image and text features using convolutional and language model.
• Generative Model
  • Map noise and convolutional features to a single 200 dimensional representation.
• Discriminator Model
  • Combine the representation of word vectors (corresponding to tags) and images.

Future Work

• While the results are not so good, they do show the potential of Conditional GANs, especially in the multimodal setting.