BigGAN
BigGAN
**BigGAN** is a type of generative adversarial network that was designed for scaling generation to high-resolution, high-fidelity images. It includes a number of incremental changes and innovations. The baseline and incremental changes are:

- Using [SAGAN](https://paperswithcode.com/method/sagan) as a baseline with spectral norm. for G and D, and using [TTUR](https://paperswithcode.com/method/ttur).
- Using a Hinge Loss [GAN](https://paperswithcode.com/method/gan) objective
- Using class-[conditional batch normalization](https://paperswithcode.com/method/conditional-batch-normalization) to provide class information to G (but with linear projection not MLP.
- Using a [projection discriminator](https://paperswithcode.com/method/projection-discriminator) for D to provide class information to D.
- Evaluating with EWMA of G's weights, similar to ProGANs.

The innovations are:

- Increasing batch sizes, which has a big effect on the Inception Score of the model.
- Increasing the width in each layer leads to a further Inception Score improvement.
- Adding skip connections from the latent variable $z$ to further layers helps performance.
- A new variant of [Orthogonal Regularization](https://paperswithcode.com/method/orthogonal-regularization).
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://paperswithcode.com/paper/large-scale-gan-training-for-high-fidelity
Large Scale GAN Training for High Fidelity Natural Image Synthesis