On Disentangled Representations Learned from Correlated Data
Abstract
The focus of disentanglement approaches has been on identifying independent factors of variation in data. However, the causal variables underlying real-world observations are often not statistically independent. In this work, we bridge the gap to real-world scenarios by analyzing the behavior of the most prominent disentanglement approaches on correlated data in a large-scale empirical study (including 4260 models). We show and quantify that systematically induced correlations in the dataset are being learned and reflected in the latent representations, which has implications for downstream applications of disentanglement such as fairness. We also demonstrate how to resolve these latent correlations, either using weak supervision during training or by post-hoc correcting a pre-trained model with a small number of labels.
Cite
Text
Träuble et al. "On Disentangled Representations Learned from Correlated Data." International Conference on Machine Learning, 2021.Markdown
[Träuble et al. "On Disentangled Representations Learned from Correlated Data." International Conference on Machine Learning, 2021.](https://mlanthology.org/icml/2021/trauble2021icml-disentangled/)BibTeX
@inproceedings{trauble2021icml-disentangled,
title = {{On Disentangled Representations Learned from Correlated Data}},
author = {Träuble, Frederik and Creager, Elliot and Kilbertus, Niki and Locatello, Francesco and Dittadi, Andrea and Goyal, Anirudh and Schölkopf, Bernhard and Bauer, Stefan},
booktitle = {International Conference on Machine Learning},
year = {2021},
pages = {10401-10412},
volume = {139},
url = {https://mlanthology.org/icml/2021/trauble2021icml-disentangled/}
}