Generators and discriminators in 3D-GAN

The architecture of the generator network of 3D-GAN is as follows:

Architecture of the generator network in 3D-GAN

The generator network consists of five transposed convolution layers (nn.ConvTranspose3d), in which the first four layers are followed by the Batch Normalization layer (nn.BatchNorm3d) and ReLU activation function, and the last layer is followed by a Sigmoid activation function. The kernel size, stride size, and padding size are set to 4, 2 and 1 in all the transposed convolution layers, respectively. Here, the input latent vector can be gradually expanded to a cube, which can be considered ...

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