Computer Science > Machine Learning
[Submitted on 30 Sep 2022 (v1), last revised 8 Oct 2022 (this version, v2)]
Title:MaskTune: Mitigating Spurious Correlations by Forcing to Explore
View PDFAbstract: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 MaskTune outperforms or achieves similar performance to the competing methods when applied to the selective classification (classification with rejection option) task. Code for MaskTune is available at this https URL.
Submission history
From: Saeid Asgari Taghanaki [view email][v1] Fri, 30 Sep 2022 19:36:12 UTC (1,280 KB)
[v2] Sat, 8 Oct 2022 19:38:28 UTC (1,280 KB)
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