f14bc21be7eaeed046fed206a492e652-Supplemental.pdf

The major differences are as follows: 1) we use dropout and BN with weight normalization (WN) as a regularizer instead of the existing techniques such as spectral normalization (SN) and gradient penalties (GP). The BN is proven to function as a regularizer imposing the Lipschitz constraint [9], which has been achieved by SN and GP [3, 7]. Plus, the dropout and WN have been successfully adopted in the classifier-based model[12]. The learning rates of the discriminator and the generator are set according to two-timescale learning rate (TTUR) [4], which is adopted in Proj. SNGAN sets the learning rates ofthe discriminator and the generator as0.0004 and 0.0001, respectively,andtheyarefixedoverthecourse ofthetraining.