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We controlled feature construction on images using masks to help models generalize to test distributions with covariate shift and noticed that it didn't affect the saliency maps in the way one would expect even though it improved generalization.

Saliency is a Possible Red Herring When Diagnosing Poor Generalization

ICLR, (2021)

Cited by: 6|Views97
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Abstract

Poor generalization is one symptom of models that learn to predict target variables using spuriously-correlated image features present only in the training distribution instead of the true image features that denote a class. It is often thought that this can be diagnosed visually using attribution (aka saliency) maps. We study if this ass...More
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Becks Simpson
Becks Simpson
Francis Dutil
Francis Dutil
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