Learning Concept Credible Models for Mitigating Shortcuts.

Adv Neural Inf Process Syst

Division of Computer Science & Engineering, University of Michigan, Ann Arbor, MI, USA.

Published: December 2022

During training, models can exploit spurious correlations as shortcuts, resulting in poor generalization performance when shortcuts do not persist. In this work, assuming access to a representation based on domain knowledge () that is invariant to shortcuts, we aim to learn robust and accurate models from biased training data. In contrast to previous work, we do not rely solely on known concepts, but allow the model to also learn unknown concepts. We propose two approaches for mitigating shortcuts that incorporate domain knowledge, while accounting for potentially important yet unknown concepts. The first approach is two-staged. After fitting a model using known concepts, it accounts for the residual using unknown concepts. While flexible, we show that this approach is vulnerable when shortcuts are correlated with the unknown concepts. This limitation is addressed by our second approach that extends a recently proposed regularization penalty. Applied to two real-world datasets, we demonstrate that both approaches can successfully mitigate shortcut learning.

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10751032PMC

Publication Analysis

Top Keywords

unknown concepts
16
mitigating shortcuts
8
domain knowledge
8
shortcuts
6
concepts
6
learning concept
4
concept credible
4
credible models
4
models mitigating
4
shortcuts training
4

Similar Publications

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!