timbmg / Vae Cvae Mnist
Variational Autoencoder and Conditional Variational Autoencoder on MNIST in PyTorch
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Variational Autoencoder & Conditional Variational Autoenoder on MNIST
VAE paper: Auto-Encoding Variational Bayes
CVAE paper: Learning Structured Output Representation using Deep Conditional Generative Models
In order to run conditional variational autoencoder, add --conditional to the the command. Check out the other commandline options in the code for hyperparameter settings (like learning rate, batch size, encoder/decoder layer depth and size).
Results
All plots obtained after 10 epochs of training. Hyperparameters accordning to default settings in the code; not tuned.
z ~ q(z|x) and q(z|x,c)
The modeled latent distribution after 10 epochs and 100 samples per digit.
| VAE | CVAE |
|---|---|
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p(x|z) and p(x|z,c)
Randomly sampled z, and their output. For CVAE, each c has been given as input once.
| VAE | CVAE |
|---|---|
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