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Tagged with variational-autoencoder pytorch
8 questions
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Any tutorials/courses to learn variational autoencoders on tabular data?
I aim to use variational autoencoders (VAE) to find interpretable latent spaces for genetic data. So, I need to understand how they work, what activation function to use, etc. But all tutorials and ...
- 111
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What best practices for VAE do you know?
The data is binary voxel data of shape (60, 36, 60). I want to compress such data into ...
0
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1
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832
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Why does the latent space in Stable Diffusion have a shape of 64x64x3?
Since the encoding is performed by a Variational Autoencoder, the VAE encoder must output some mean and log variance that we can ...
5
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2
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How to generate new data given a trained VAE - sample from the learned latent space or from multivariate Gaussian?
To generate synthetic dataset using a trained VAE, there is confusion between two approaches:
Use learned latent space: z = mu + (eps * log_var) to generate (...
- 235
1
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Why does the VAE using a KL-divergence with a non-standard mean does not produce good images?
I know I can make a VAE do generation with a mean of 0 and std-dev of 1.
I tested it with the following loss function:
...
- 53
7
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2
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3k
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How is this Pytorch expression equivalent to the KL divergence?
I found the following PyTorch code (from this link)
-0.5 * torch.sum(1 + sigma - mu.pow(2) - sigma.exp())
where mu is the mean ...
- 805
1
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401
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variational auto encoder loss goes down but does not reconstruct input. out of debugging ideas
My variational autoencoder seems to work for MNIST, but fails on slightly "harder" data.
By "fails" I mean there are at least two apparent problems:
Very poor reconstruction, for ...
- 789
2
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Why is my variational auto-encoder generating random noise?
This is my first variational autoencoder. Background info: I am using the MNIST digits dataset. The model is created and trained in PyTorch. The model is able to get a reasonably low loss, but the ...
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