A minimal Pytorch implementation of "SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization" by Sony.

• Paper (arxiv) : SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization
• Official codes : sony/sqvae

This repository provides a simple implementation of SQ-VAE which extends the standard VAE via stochastic dequantization and quantization.

This repository includes:

• Architecture : Conv/ResNet-based encoder and decoder with stochastic quantizer
• Training codes with MNIST dataset

Let's boost your VQ-VAEs application by using SQ-VAE.

• Python 3.8.*
• numpy **
• pytorch **
• torchvision **
• tensorboard **
• tqdm **

Make sure to download the MNIST dataset.

Specify hyperparameters inside a .yaml file, e.g.:

# resize MNIST from 28 to 32
# to apply hier compression
data_resize: 32 

batch_size: 128
lr: 0.001
beta_1: 0.0
beta_2: 0.99
num_epoch: 100
temperature_decay: 0.00001

encdec:
    in_ch: 1 # MNIST
    width: 8
    depth: 2
    num_down: 4 # compression -> 2^4=16
    stride: 2

quantizer:
    size_dict: 32
    dim_dict: 8
    var_q_init: 5.

python train_sqvae.py --datadir <path_to_mnist> --jobname <job_name>

To see more options:

python train_sqvae.py -h

• Input size : 32 x 32 (resized from 28 x 28)
• Codebook size : 32
• Latent size : 2 x 2

Left half -> input images / Right half -> reconstruction images

[1] Takida, Y., Shibuya, T., Liao, W., Lai, C., Ohmura, J., Uesaka, T., Murata, N., Takahashi S., Kumakura, T. and Mitsufuji, Y., "SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization," 39th International Conference on Machine Learning.

@INPROCEEDINGS{takida2022sq-vae,
    author={Takida, Yuhta and Shibuya, Takashi and Liao, WeiHsiang and Lai, Chieh-Hsin and Ohmura, Junki and Uesaka, Toshimitsu and Murata, Naoki and Takahashi, Shusuke and Kumakura, Toshiyuki and Mitsufuji, Yuki},
    title={{SQ-VAE}: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantization},
    booktitle={International Conference on Machine Learning},
    year={2022},
    }