Publication | Conference on Neural Information Processing Systems 2022
MaskTune
Mitigating Spurious Correlations by Forcing to Explore
Learning the right features during training is a significant challenge for deep neural networks (DNNs). DNNs might instead pick up spurious features. This work investigates a novel solution to this problem.
Code for MaskTune is available at https://github.com/aliasgharkhani/Masktune.
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MaskTune: Mitigating Spurious Correlations by Forcing to Explore
Saeid Asgari, Aliasghar Khani, Fereshte Khani, Ali Gholami, Linh Tran, Ali Mahdavi-Amiri, Ghassan Hamarneh
Conference on Neural Information Processing Systems 2022
A fundamental challenge of over-parameterized deep learning models is learning meaningful data representations that yield good performance on a downstream task without over-fitting spurious input features. This work proposes MaskTune, a masking strategy that prevents over-reliance on spurious (or a limited number of) features. MaskTune forces the trained model to explore new features during a single epoch finetuning by masking previously discovered features. MaskTune, unlike earlier approaches for mitigating shortcut learning, does not require any supervision, such as annotating spurious features or labels for subgroup samples in a dataset. Our empirical results on biased MNIST, CelebA, Waterbirds, and ImagenNet-9L datasets show that MaskTune is effective on tasks that often suffer from the existence of spurious correlations. Finally, we show that \method{} outperforms or achieves similar performance to the competing methods when applied to the selective classification (classification with rejection option) task.
Associated Researchers
Fereshte Khani
Stanford University
Linh Tran
Autodesk AI Lab
Ghassan Hamarneh
School of Computing Science, Simon Fraser University
Ali Mahdavi-Amiri
School of Computing Science, Simon Fraser University
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