May 2019
Intermediate to advanced
272 pages
7h 19m
English
Content preview from Hands-On Generative Adversarial Networks with Keras
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The discriminator
The discriminator that is used in Progressive Growing of GANs consists of stacks of convolutions followed by downsampling layers. Each convolution has a WeightScalingLayer layer that normalizes the layer outputs using a constant, and a PixelNormLayer layer that normalizes the outputs by their L2 norm, thus ensuring that the outputs vector has unit length.
Specific to the Progressive Growing of GANs methodology, this discriminator architecture has a BlockSelectionLayer layer, which defines the layer that should be used as the output during training. Remember, in this methodology, we first train an output layer at a low resolution and then train the other layers that output a higher resolution one by one.
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