Know Your Limits: Uncertainty Estimation with ReLU Classifiers Fails at Reliable OOD Detection

• URL: http://arxiv.org/abs/2012.05329v3
• Date: Fri, 26 Feb 2021 09:56:51 GMT
• Title: Know Your Limits: Uncertainty Estimation with ReLU Classifiers Fails at Reliable OOD Detection
• Authors: Dennis Ulmer and Giovanni Cin\`a
• Abstract summary: This paper gives a theoretical explanation for said experimental findings and illustrates it on synthetic data. We prove that such techniques are not able to reliably identify OOD samples in a classification setting.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: A crucial requirement for reliable deployment of deep learning models for safety-critical applications is the ability to identify out-of-distribution (OOD) data points, samples which differ from the training data and on which a model might underperform. Previous work has attempted to tackle this problem using uncertainty estimation techniques. However, there is empirical evidence that a large family of these techniques do not detect OOD reliably in classification tasks. This paper gives a theoretical explanation for said experimental findings and illustrates it on synthetic data. We prove that such techniques are not able to reliably identify OOD samples in a classification setting, since their level of confidence is generalized to unseen areas of the feature space. This result stems from the interplay between the representation of ReLU networks as piece-wise affine transformations, the saturating nature of activation functions like softmax, and the most widely-used uncertainty metrics.

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