Investigation of machine learning algorithms robust to changes between the training and test distributions is an active area of research. In this paper we explore a special type of dataset shift which we call class-dependent domain shift. It is characterized by the following features: the input data causally depends on the label, the shift in the data is fully explained by a known variable, the variable which controls the shift can depend on the label, there is no shift in the label distribution. We define a simple optimization problem with an information theoretic constraint and attempt to solve it with neural networks. Experiments on a toy dataset demonstrate the proposed method is able to learn robust classifiers which generalize well to unseen domains.
@article{arxiv.2007.05335,
title = {Robust Classification under Class-Dependent Domain Shift},
author = {Tigran Galstyan and Hrant Khachatrian and Greg Ver Steeg and Aram Galstyan},
journal= {arXiv preprint arXiv:2007.05335},
year = {2020}
}
Comments
Accepted at ICML 2020 workshop on Uncertainty and Robustness in Deep Learning