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Summary of "Conditional Generative Adversarial Nets" Paper

Conditional Generative Adversarial Nets

Introduction

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.
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