Deep convolutional GAN (DCGAN)
Proposed in 2016, DCGANs have become one of the most popular and successful GAN architectures. The main idea of the design was using convolutional layers without the use of pooling layers or the end classifier layers. The convolutional strides and transposed convolutions are employed for the downsampling (the reduction of dimensions) and upsampling (the increase of dimensions. In GANs, we do this with the help of a transposed convolution layer. To know more about transposed convolution layers, refer to the paper A guide to convolution arithmetic for deep learning by Dumoulin and Visin) of images.
Before going into the details of the DCGAN architecture and its capabilities, let us point out the major changes that were introduced in the paper:
- The network consisted of all convolutional layers. The pooling layers were replaced by strided convolutions (i.e., instead of one single stride while using the convolutional layer, we increased the...
