Tailored Uncertainty Estimation for Deep Learning Systems

• URL: http://arxiv.org/abs/2204.13963v1
• Date: Fri, 29 Apr 2022 09:23:07 GMT
• Title: Tailored Uncertainty Estimation for Deep Learning Systems
• Authors: Joachim Sicking, Maram Akila, Jan David Schneider, Fabian H\"uger, Peter Schlicht, Tim Wirtz, Stefan Wrobel
• Abstract summary: We propose a framework that guides the selection of a suitable uncertainty estimation method. Our framework provides strategies to validate this choice and to uncover structural weaknesses. It anticipates prospective machine learning regulations that require evidences for the technical appropriateness of machine learning systems.
• Score: 10.288326973530614
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Uncertainty estimation bears the potential to make deep learning (DL) systems more reliable. Standard techniques for uncertainty estimation, however, come along with specific combinations of strengths and weaknesses, e.g., with respect to estimation quality, generalization abilities and computational complexity. To actually harness the potential of uncertainty quantification, estimators are required whose properties closely match the requirements of a given use case. In this work, we propose a framework that, firstly, structures and shapes these requirements, secondly, guides the selection of a suitable uncertainty estimation method and, thirdly, provides strategies to validate this choice and to uncover structural weaknesses. By contributing tailored uncertainty estimation in this sense, our framework helps to foster trustworthy DL systems. Moreover, it anticipates prospective machine learning regulations that require, e.g., in the EU, evidences for the technical appropriateness of machine learning systems. Our framework provides such evidences for system components modeling uncertainty.

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