Awesome

DU-VAE

This is the pytorch implementation of the paper "Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness"

Acknowledgements

Our code is mainly based on this public code. Very thanks for its authors.

Requirements

• Python >= 3.6
• Pytorch >= 1.5.0

Data

Datastes used in this paper can be downloaded in this link, with the specific license if that is not based on MIT License.

Usage

Example script to train DU-VAE on text data:

python text.py --dataset yelp \
 --device cuda:0  \
--gamma 0.5 \
--p_drop 0.2 \
--delta_rate 1 \
--kl_start 0 \
--warm_up 10

Example script to train DU-VAE on image data:

python3.6 image.py --dataset omniglot \
 --device cuda:3 \
--kl_start 0 \
--warm_up 10 \
--gamma 0.5  \
--p_drop 0.1 \
--delta_rate 1 \
--dataset omniglot

Example script to train DU-IAF, a variant of DU-VAE, on text data:

python3.6 text_IAF.py --device cuda:2 \
--dataset yelp \
--gamma 0.6 \
--p_drop 0.3 \
--delta_rate 1 \
--kl_start 0 \
--warm_up 10 \
--flow_depth 2 \
--flow_width 60

Example script to train DU-IAF on image data:

python3.6 image_IAF.py --dataset omniglot\
  --device cuda:3 \
--kl_start 0 \
--warm_up 10 \
--gamma 0.5 \
 --p_drop 0.15\
 --delta_rate 1 \
--flow_depth 2\
--flow_width 60 

Here,

• --dataset specifies the dataset name, currently it supports synthetic, yahoo, yelp for text.py and omniglot for image.py.
• --kl_start represents starting KL weight (set to 1.0 to disable KL annealing)
• --warm_up represents number of annealing epochs (KL weight increases from kl_start to 1.0 linearly in the first warm_up epochs)
• --gamma represents the parameter $\gamma$ in our Batch-Normalization approach, which should be more than 0 to use our model.
• --p_drop represents the parameter $1-p$ in our Dropout approach, which denotes the percent of data to be ignored and should be ranged in (0,1).
• --delta_rate represents the hyper-parameter $\alpha$ to controls the min value of the variance $\delta^2$
• --flow_depth represents number of MADE layers used to implement DU-IAF.
• --flow_wdith controls the hideen size in each IAF block, where we set the product between the value and the dimension of $z$ as the hidden size. For example, when we set --flow width 60 with the dimension of $z$ as 32, the hidden size of each IAF block is 1920.

Reference

If you find our methods or code helpful, please kindly cite the paper:

@inproceedings{shen2021regularizing,
  title={Regularizing Variational Autoencoder with Diversity and Uncertainty Awareness},
  author={Shen, Dazhong  and Qin, Chuan and Wang, Chao and Zhu, Hengshu and Chen, Enhong and Xiong, Hui},
  booktitle={Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI-21)},
  year={2021}
}