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View Code? Open in Web Editor NEWCode for "On the Effects of Batch and Weight Normalization in Generative Adversarial Networks"
License: GNU General Public License v3.0
Code for "On the Effects of Batch and Weight Normalization in Generative Adversarial Networks"
License: GNU General Public License v3.0
Thanks for sharing interesting, practical work. I am thinking about replacing BN to proposed WN layer for speech GAN.
Is it possible to apply 'WeightNormalizedConv2d' & 'WeightNormalizedConvTranspose2d' layer to 1d convolution/deconvolution by simply put kernel size as tuple (H, window_size)? H is height of data.
Or can we think about new module with F.conv1d?
For example, its forward computation looks like this
def forward(self, input):
return self.norm_scale_bias(F.conv1d(input, self.weight, None, self.stride,
self.padding, self.dilation, 1))
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