sagan-pytorch
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Self-Attention Generative Adversarial Networks Implementation in PyTorch
I save model during training with G: 1.42529; D: 1.17999 After loading discriminator and generator I get G: -26.45726; D: 0.89195 It looks like discriminator behaves differently. Removing 'd_optimizer.step' gives...
I used the mini-imagenet data set, but the result was very poor. How can I improve it?
self.conv = nn.ModuleList([ConvBlock(512, 512, n_class=n_class), ConvBlock(512, 512, n_class=n_class), ConvBlock(512, 512, n_class=n_class, self_attention=True), ConvBlock(512, 256, n_class=n_class), ConvBlock(256, 128, n_class=n_class)])