GauGAN | ACM SIGGRAPH 2019 Real-Time Live!

We propose GauGAN, a GAN-based image synthesis model that can generate photo-realistic images given an input semantic layout. It is built on spatially-adaptive normalization, a simple but effective normalization layer. Previous methods directly feed the semantic layout as input to the deep network, which is then processed through stacks of convolution, normalization, and non-linearity layers. We show that this is sub-optimal as the normalization layers tend to “wash away” semantic information. To address th

Source: GauGAN | ACM SIGGRAPH 2019 Real-Time Live!

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