The simple Keras implementation of ICLR 2018 paper, Spectral Normalization for Generative Adversarial Networks. [openreview][arixiv][original code(chainer)]

[Hackmd][github]

10epoch	With SN	Without SN
With GP
Without GP

100epoch	With SN	Without SN
With GP
Without GP

200epoch	With SN	Without SN
With GP
Without GP

300epoch	With SN	Without SN
With GP
Without GP

400epoch	With SN	Without SN
With GP
Without GP

500epoch	with SN	without SN
With GP
Without GP

Loss	with SN	without SN
With GP
Without GP

10epoch	With SN	Without SN
With GP
Without GP

100epoch	With SN	Without SN
With GP
Without GP

200epoch	With SN	Without SN
With GP
Without GP

300epoch	With SN	Without SN
With GP
Without GP

400epoch	With SN	Without SN
With GP
Without GP

500epoch	with SN	without SN
With GP
Without GP

Loss	with SN	without SN
With GP
Without GP

1. Move SpectralNormalizationKeras.py in your dir
2. Import these layer class

from SpectralNormalizationKeras import DenseSN, ConvSN1D, ConvSN2D, ConvSN3D

3. Use these layers in your discriminator as usual

CIFAR10 with DCGAN architecture

CIFAR10 with ResNet architecture

• Compare with WGAN-GP
• Projection Discriminator

• Thank @anshkapil pointed out and @IFeelBloated corrected this implementation.