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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 ...
jdw136's user avatar
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1 vote
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29 views

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 ...
Yulia Kentieva's user avatar
1 vote
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684 views

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: ...
axon's user avatar
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1 vote
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401 views

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 ...
Gulzar's user avatar
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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 ...
Renat Abdrakhmanov's user avatar