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 as a baseline with spectral norm. for G and D, and using TTUR.
• Using a Hinge Loss GAN objective
• Using class-conditional batch normalization to provide class information to G (but with linear projection not MLP.
• Using a 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.