DCGAN

PyTorch implementation of Deep Convolutional Generative Adversarial Networks (DCGAN)

Network architecture

• Generator

  • hidden layers: Four 4x4 strided convolutional layers (1024, 512, 256, and 128 kernels, respectively) with ReLU
  • output layer: 4x4 strided convolutional layer (4096 nodes = 64x64 size image) with Tanh
  • Batch normalization is used except for output layer

• Discriminator

  • hidden layers: Four 4x4 convolutional layers (128, 256, 512, and 1024 kernels, respectively) with Leaky ReLU
  • output layer: 4x4 convolutional layer (1 node) with Sigmoid
  • Batch normalization is used except for 1st hidden layer & output layer

Generating MNIST dataset

• MNIST image is resized to 64x64 size image

Results

• For learning rate = 0.0002 (Adam optimizer), batch size = 128, # of epochs = 20:

GAN losses	Generated images

Generating CelebA dataset

• 108x108 center region of CelebA image is cropped, and resized to 64x64 size image

Results

• For learning rate = 0.0002 (Adam optimizer), batch size = 128, # of epochs = 20:

GAN losses	Generated images

References

1. https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/03-advanced/deep_convolutional_gan
2. https://github.com/znxlwm/pytorch-MNIST-CelebA-GAN-DCGAN
3. https://github.com/moono/moo-dl-practice/tree/master/Work-place/DCGAN-MNIST
4. https://github.com/moono/moo-dl-practice/tree/master/Work-place/DCGAN-celebA