Improving the quality of synthesized images using a convolutional and Wasserstein GAN
In this section, we will implement a DCGAN, which will enable us to improve the performance we saw in the previous GAN example. Additionally, we will employ several extra key techniques and implement a Wasserstein GAN (WGAN).
The techniques that we will cover in this section will include the following:
- Transposed convolution
- BatchNorm
- WGAN
- Gradient penalty
The DCGAN was proposed in 2016 by A. Radford, L. Metz, and S. Chintala in their article Unsupervised representation learning with deep convolutional generative adversarial networks, which is freely available at https://arxiv.org/pdf/1511.06434.pdf. In this article, the researchers proposed using convolutional layers for both the generator and discriminator networks. Starting from a random vector, z, the DCGAN first uses a fully connected layer to project z into a new vector with a proper size so that it can be reshaped...
