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  4. Safety Performance of Neural Networks in the Presence of Covariate Shift
 
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2024
Conference Paper
Title

Safety Performance of Neural Networks in the Presence of Covariate Shift

Abstract
Covariate shift may impact the operational safety performance of neural networks. A re-evaluation of the safety performance, however, requires collecting new operational data and creating corresponding ground truth labels, which often is not possible during operation. We are therefore proposing to reshape the initial test set, as used for the safety performance evaluation prior to deployment, based on an approximation of the operational data. This approximation is obtained by observing and learning the distribution of activation patterns of neurons in the network during operation. The reshaped test set reflects the distribution of neuron activation values as observed during operation, and may therefore be used for re-evaluating safety performance in the presence of covariate shift. First, we derive conservative bounds on the values of neurons by applying finite binning and static dataflow analysis. Second, we formulate a mixed integer linear programming (MILP) constraint for constructing the minimum set of data points to be removed in the test set, such that the difference between the discretized test and operational distributions is bounded. We discuss potential benefits and limitations of this constraint-based approach based on our initial experience with an implemented research prototype.
Author(s)
Cheng, Chih-Hong  
Fraunhofer-Institut für Kognitive Systeme IKS  
Ruess, Harald
fortiss GmbH  
Theodorou, Konstantinos
Fraunhofer-Institut für Kognitive Systeme IKS  
Mainwork
Verified Software. Theories, Tools and Experiments. 15th International Conference, VSTTE 2023  
Project(s)
IKS-Ausbauprojekt  
Funder
Bayerisches Staatsministerium für Wirtschaft, Landesentwicklung und Energie
Conference
International Conference on Verified Software - Theories, Tools, and Experiments 2023  
DOI
10.1007/978-3-031-66064-1_2
Language
English
Fraunhofer-Institut für Kognitive Systeme IKS  
Fraunhofer Group
Fraunhofer-Verbund IUK-Technologie  
Keyword(s)
  • neural networks

  • safety

  • safety performance

  • covariate shift

  • finite binning

  • static dataflow

  • mixed integer linear programming

  • MILP

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