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Towards Structured Evaluation of Deep Neural Network Supervisors
Semcon AB, Gothenburg, Sweden.
University of Gothenburg, Chalmers Institute of Technology, Gothenburg, Sweden.
RISE Research Institutes of Sweden AB, Lund and Gothenburg, Sweden.
Machine Learning and AI Center of Excellence, Volvo Cars, Gothenburg, Sweden.
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2019 (English)In: 2019 IEEE International Conference On Artificial Intelligence Testing (AITest), New York: IEEE, 2019, p. 27-34Conference paper, Published paper (Refereed)
Abstract [en]

Deep Neural Networks (DNN) have improved the quality of several non-safety related products in the past years. However, before DNNs should be deployed to safety-critical applications, their robustness needs to be systematically analyzed. A common challenge for DNNs occurs when input is dissimilar to the training set, which might lead to high confidence predictions despite proper knowledge of the input. Several previous studies have proposed to complement DNNs with a supervisor that detects when inputs are outside the scope of the network. Most of these supervisors, however, are developed and tested for a selected scenario using a specific performance metric. In this work, we emphasize the need to assess and compare the performance of supervisors in a structured way. We present a framework constituted by four datasets organized in six test cases combined with seven evaluation metrics. The test cases provide varying complexity and include data from publicly available sources as well as a novel dataset consisting of images from simulated driving scenarios. The latter we plan to make publicly available. Our framework can be used to support DNN supervisor evaluation, which in turn could be used to motive development, validation, and deployment of DNNs in safety-critical applications. © 2019 IEEE.

Place, publisher, year, edition, pages
New York: IEEE, 2019. p. 27-34
Keywords [en]
deep neural networks, robustness, out-of-distribution, supervisor, automotive perception
National Category
Other Engineering and Technologies
Identifiers
URN: urn:nbn:se:hh:diva-41102DOI: 10.1109/AITest.2019.00-12ISI: 000470916100005Scopus ID: 2-s2.0-85067113703ISBN: 978-1-7281-0492-8 (print)OAI: oai:DiVA.org:hh-41102DiVA, id: diva2:1375173
Conference
EEE International Conference On Artificial Intelligence Testing (AITest), San Francisco, CA, USA, 4-9 April, 2019
Funder
VinnovaWallenberg AI, Autonomous Systems and Software Program (WASP)
Note

Funding Agency: Fordonsstrategisk forskning och innovation (FFI) Grant Number: 2017-03066

Available from: 2019-12-04 Created: 2019-12-04 Last updated: 2019-12-19Bibliographically approved

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Englund, Cristofer

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CiteExportLink to record
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Citation style
  • apa
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Output format
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