Monitoring Shortcut Learning using Mutual Information

• URL: http://arxiv.org/abs/2206.13034v1
• Date: Mon, 27 Jun 2022 03:55:23 GMT
• Title: Monitoring Shortcut Learning using Mutual Information
• Authors: Mohammed Adnan, Yani Ioannou, Chuan-Yung Tsai, Angus Galloway, H.R. Tizhoosh, Graham W. Taylor
• Abstract summary: Shortcut learning is evaluated on real-world data that does not contain spurious correlations. Experiments demonstrate that MI can be used as a metric network shortcut network.
• Score: 16.17600110257266
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The failure of deep neural networks to generalize to out-of-distribution data is a well-known problem and raises concerns about the deployment of trained networks in safety-critical domains such as healthcare, finance and autonomous vehicles. We study a particular kind of distribution shift $\unicode{x2013}$ shortcuts or spurious correlations in the training data. Shortcut learning is often only exposed when models are evaluated on real-world data that does not contain the same spurious correlations, posing a serious dilemma for AI practitioners to properly assess the effectiveness of a trained model for real-world applications. In this work, we propose to use the mutual information (MI) between the learned representation and the input as a metric to find where in training, the network latches onto shortcuts. Experiments demonstrate that MI can be used as a domain-agnostic metric for monitoring shortcut learning.

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