This is an implementation of the VQ-VAE (Vector Quantized Variational Autoencoder) and Convolutional Varational Autoencoder. from Neural Discrete representation learning for compressing MNIST and Cifar10. The code is based upon pytorch/examples/vae.
pip install -r requirements.txt
python main.py- Python 3.6 (maybe 3.5 will work as well)
- PyTorch 0.4
- Additional requirements in requirements.txt
# For example
python3 main.py --dataset=cifar10 --model=vqvae --data-dir=~/.datasets --epochs=3All images are taken from the test set. Top row is the original image. Bottom row is the reconstruction.
k - number of elements in the dictionary. d - dimension of elements in the dictionary (number of channels in bottleneck).
- MNIST (k=10, d=64)
- CIFAR10 (k=128, d=256)
- Imagenet (k=512, d=128)
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Implement Continuous Relaxation Training of Discrete Latent Variable Image Models
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Sample using PixelCNN prior
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Improve results on cifar - nearest neighbor should be performed to 10 dictionaries rather than 1
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Improve results on cifar - replace MSE with NLL
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Improve results on cifar - measure bits/dim
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Compare architecture with the offical one
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Merge VAE and VQ-VAE for MNIST and Cifar to one script
tf-vaevae for a good reference.
