Deep convolutional GAN (DCGAN)
Proposed in 2016, DCGANs have become one of the most popular and successful GAN architectures. The main idea in 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 and upsampling 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 in the discriminator and transposed convolutions in the generator.
- The fully connected classifying layers after the convolutions are removed.
- To help with the gradient flow, batch normalization is done after every convolutional layer.
The basic idea of DCGANs is same as the vanilla GAN: we have a generator that takes in noise of 100 dimensions; the...
