Multi-View Total Correlation Auto-Encoder

The official implementation for the paper "Multi-View Representation Learning via Total Correlation Objective" in NeurIPS 2021.

1. Prerequisites

First of all, please download PolyMNIST dataset, pretrained classifiers, and inception network here and unzip them under the data directory. Please note that all the material downloaded are from the official implementation of MoPoE-VAE.

Then, install following libraries and packages:

• Python version 3.5.6
• PyTorch version 1.4.0
• CUDA version 11.0
• The conda environment defined in environment.yml

Alternatively, you can do following steps for easier installation:

conda env create -f environment.yml  # create conda env
conda activate mvtcae                # activate conda env

2. Train & evaluate MVTCAE

Experiments can be started by running main_mmnist.py as follows:

python main_mmnist.py

To choose among running the MVAE, MMVAE, mmJSD, MoPoE-VAE, and MVTCAE, you need to change the value of variable METHOD in main_mmnist.py to 'poe', 'moe', 'jsd', 'joint_elbo', or 'tc' respectively.

By default, each experiment uses METHOD='tc'.

You can find evaluation results in the tensorboard log saved in exp_poly directory.

3. Results

4. Notes

All the results are averaged over 5 different seeds (0~4).

We appreciate MoPoE-VAE on which our implementation is heavly based.