This repository contains the official Tensorflow implementation of the paper "Deep Probabilistic Canonical Correlation Analysis" presented in AAAI 2021.
Abstract
We propose a deep generative framework for multi-view learning based on a probabilistic interpretation of canonical correlation analysis (CCA). The model combines a linear multi-view layer in the latent space with deep generative networks as observation models, to decompose the variability in multiple views into a shared latent representation that describes the common underlying sources of variation and a set of viewspecific components. To approximate the posterior distribution of the latent multi-view layer, an efficient variational inference procedure is developed based on the solution of probabilistic CCA. The model is then generalized to an arbitrary number of views. An empirical analysis confirms that the proposed deep multi-view model can discover subtle relationships between multiple views and recover rich representations.
The latest release of the code is tested with:
python 3.6tensorflow 1.14.0
Dependencies can be installed via
pip install -r requirements.txt
To train the model, simply run:
python main.py --mode train
For more specific training, run the following scripts inside the directory: ./exp :
-
Two-View Noisy MNIST:
source Noisy_MNIST_2V.sh -
Multi-Modal YaleB (Facial Components):
source YaleB_multimodal.shReconstruction of missing views
Samples of the available views, at the top, and their corresponding reconstructed views where in (b) the model extracts the 4 facial components based on the complete face image, and in (d) the model reconstructs the whole face and the one facial component (right eye) based on its 3 partial face complements (left eye+ nose+mouth).
Please cite our paper if you use this code in your research work.
@inproceedings{karami2021deep,
title={Deep Probabilistic Canonical Correlation Analysis},
author={Karami, Mahdi and Schuurmans, Dale},
booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
pages={8055--8063},
year={2021}
}
Please, submit a Github issue or contact karami1@ualberta.ca .