A Variational Autoencoder (VAE) implemented in PyTorch
License: BSD 3-Clause "New" or "Revised" License
See this blog post: http://kvfrans.com/variational-autoencoders-explained/
Variational Autoencoder is introduced in this paper https://arxiv.org/abs/1312.6114
Also this tutorial paper: https://arxiv.org/abs/1606.05908
Got this error -
Using a target size (torch.Size([128, 1, 28, 28])) that is different to the input size (torch.Size([128, 784])) is deprecated.
Any thoughts?
I am pretty sorry to put this issue here.
The results I run are not good. All generated images are pretty similar to others, which are different to the results in the blogs and tutorial. I think it is mainly because you use MSE（Mean Squared Error） as the loss function, which calculates the Euclidean distance between two images. MSELoss can not measure the likelihood of images well and Figure 3 of the tutorial talks about this question.
I also change the output layer's ReLU to sigmoid in decoder to make the results well.
return mu + sigma * Variable(std_z, requires_grad=False) # Reparameterization trick
the line 70 code in the vae.py, I think the mu and std_z need grad, but sigma do not need grad, because it present the sample
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