Conditional Generative Adversarial Nets

Code

• github.com/Sinestro38/qosf-qgan
  none★ 27
• github.com/bhiziroglu/Conditional-Generative-Adversarial-Network
  pytorch★ 7
• github.com/jacksonkaunismaa/neural-music
  pytorch★ 6
• github.com/catalyst-team/gan
  pytorch★ 5
• github.com/doansangg/CGAN-PyTorch
  pytorch★ 0
• github.com/HyperZealot/mxnet-cgan
  tf★ 0
• github.com/YigitGunduc/Conditional-GANs-CGANs
  tf★ 0
• github.com/SaubanMusaddiq/Conditional-Gans
  none★ 0
• github.com/pmsharkKOR/Deep-Learning-Examples---Awesome-Resourse1
  tf★ 0
• github.com/MCLYang/RhythmGAN_pytorch
  pytorch★ 0

Abstract

Generative Adversarial Nets [8] were recently introduced as a novel way to train generative models. In this work we introduce the conditional version of generative adversarial nets, which can be constructed by simply feeding the data, y, we wish to condition on to both the generator and discriminator. We show that this model can generate MNIST digits conditioned on class labels. We also illustrate how this model could be used to learn a multi-modal model, and provide preliminary examples of an application to image tagging in which we demonstrate how this approach can generate descriptive tags which are not part of training labels.

Tasks

Benchmark Results

Reproductions