In the original GAN framework proposal, the authors used only fully-connected networks. The first major improvement of the GAN framework is Deep Convolutional Generative Adversarial networks (DCGANs). In this new architecture, both the generator and the discriminator are convolutional networks. They have some constraints, which help to stabilize the training:
- The discriminator uses strided convolutions instead of pooling layers.
- The generator is a special type of CNN, which uses fractional-strided convolutions to increase the size of the images. We'll discuss it in the next section.
- Both networks use batch normalization.
- No fully-connected layers, with the exception of the last layer of the discriminator.
