Shortcut Learning in Deep Neural Networks

This paper establishes a framework for looking at out-of-distribution generalization failures of modern deep learning as the models learning false shortcuts that are present in the training data. The paper characterizes why and when shortcut learning can happen and gives recommendations for how to counter its effect. Abstract: Deep learning has triggered the current rise of artificial intelligence and is the workhorse of today’s machine intelligence. Numerous success stori