Using GANs for style transfer
So far, we have only looked at DCGANs in detail. Although there exist hundreds of different types of GAN models already, and many more are in the making, some of the well-known GAN models include the following:
- GAN
- DCGAN
- Pix2Pix
- CycleGAN
- SuperResolutionGAN (SRGAN)
- Context encoders
- Text-2-Image
- LeastSquaresGAN (LSGAN)
- SoftmaxGAN
- WassersteinGAN
Each of these GAN variants differ by either the application they are catering to, their underlying model architecture, or due to some tweaks in their optimization strategy, such as modifying the loss function. For example, SRGANs are used to enhance the resolution of a low-resolution image. The CycleGAN uses two generators instead of one, and the generators consist of ResNet-like blocks. The LSGAN uses the mean square error as the discriminator loss function instead of the usual cross-entropy loss used in most GANs.
It is impossible to discuss all of these GAN...
