GAN — DCGAN (Deep convolutional generative adversarial networks)

DCGAN is one of the popular and successful network design for GAN. It mainly composes of convolution layers without max pooling or fully connected layers. It uses convolutional stride and transposed convolution for the downsampling and the upsampling. The figure below is the network design for the generator.

Source
  • Use transposed convolution for upsampling.
  • Eliminate fully connected layers.
  • Use Batch normalization except the output layer for the generator and the input layer of the discriminator.
  • Use ReLU in the generator except for the output which uses tanh.
  • Use LeakyReLU in the discriminator.

Reference

Unsupervised representation learning with Deep convolutional generative adversarial networks

Deep Learning

Deep Learning