Unsupervised Model Selection for Variational Disentangled Representation Learning
Sunny Duan, Loic Matthey, Andre Saraiva, Nick Watters, Chris Burgess, Alexander Lerchner, Irina Higgins
Abstract
Disentangled representations have recently been shown to improve fairness, data efficiency and generalisation in simple supervised and reinforcement learning tasks. To extend the benefits of disentangled representations to more complex domains and practical applications, it is important to enable hyperparameter tuning and model selection of existing unsupervised approaches without requiring access to ground truth attribute labels, which are not available for most datasets. This paper addresses this problem by introducing a simple yet robust and reliable method for unsupervised disentangled model selection. We show that our approach performs comparably to the existing supervised alternatives across 5400 models from six state of the art unsupervised disentangled representation learning model classes. Furthermore, we show that the ranking produced by our approach correlates well with the final task performance on two different domains.
BibTeX
@inproceedings{
Duan2020Unsupervised,
title={Unsupervised Model Selection for Variational Disentangled Representation Learning},
author={Sunny Duan and Loic Matthey and Andre Saraiva and Nick Watters and Chris Burgess and Alexander Lerchner and Irina Higgins},
booktitle={International Conference on Learning Representations},
year={2020},
url={https://openreview.net/forum?id=SyxL2TNtvr}
}