Generative Adversarial Networks

DCGAN, or Deep Convolutional GAN, is a generative adversarial network architecture. It uses a couple of guidelines, in particular:

  • Replacing any pooling layers with strided convolutions (discriminator) and fractional-strided convolutions (generator).
  • Using batchnorm in both the generator and the discriminator.
  • Removing fully connected hidden layers for deeper architectures.
  • Using ReLU activation in generator for all layers except for the output, which uses tanh.
  • Using LeakyReLU activation in the discriminator for all layer.
Source: Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks

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