BigGAN-deep
BigGAN-deep
**BigGAN-deep** is a deeper version (4x) of [BigGAN](https://paperswithcode.com/method/biggan).  The main difference is a slightly differently designed [residual block](https://paperswithcode.com/method/residual-block). Here the $z$ vector is concatenated with the conditional vector without splitting it into chunks.  It is also based on residual blocks with bottlenecks. BigGAN-deep uses a different strategy than BigGAN aimed at preserving identity throughout the skip connections. In G, where the number of channels needs to be reduced, BigGAN-deep simply retains the first group of channels and drop the rest to produce the required number of channels. In D, where the number of channels should be increased, BigGAN-deep passes the input channels unperturbed, and concatenates them with the remaining channels produced by a 1 × 1 [convolution](https://paperswithcode.com/method/convolution). As far as the
network configuration is concerned, the discriminator is an exact reflection of the generator. 

There are two blocks at each resolution (BigGAN uses one), and as a result BigGAN-deep is four times
deeper than BigGAN. Despite their increased depth, the BigGAN-deep models have significantly
fewer parameters mainly due to the bottleneck structure of their residual blocks.
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