Wang, S., Wang, P., Mihaylova, L. orcid.org/0000-0001-5856-2223 et al. (1 more author) (2021) Real-time activation pattern monitoring and uncertainty characterisation in image classification. In: de Villiers, P., de Waal, A. and Gustafsson, F., (eds.) 2021 IEEE 24th International Conference on Information Fusion (FUSION). 24th International Conference on Information Fusion (Fusion 2021), 01-04 Nov 2021, Sun City, South Africa. Institute of Electrical and Electronics Engineers ISBN 9781665414272
Abstract
Deep neural networks (DNNs) have become very popular recently and have proven their potential especially for image classification. However, their performance depends significantly on the network structure and data quality. This paper investigates the performance of DNNs and especially of faster region based convolutional neural networks (R-CNNs), called faster R-CNN when the network testing data differ significantly from the training data. This paper proposes a framework for monitoring the neuron patterns within a faster RCNN by representing distributions of neuron activation patterns and by calculating corresponding distances between them, with the Kullback-Leibler divergence. The patterns of the activation states of ‘neurons’ within the network can therefore be observed if the faster R-CNN is ‘outside the comfort zone’, mostly when it works with noisy data and data that are significantly different from those used in the training stage. The validation is performed on publicly available datasets: MNIST [1] and PASCAL [2] and demonstrates that the proposed framework
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2021 ISIF. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Activation Pattern; Run-time Monitoring; Faster R-CNN; Reliability; Decision Making; Uncertainty Quantification |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Funding Information: | Funder Grant number Engineering and Physical Sciences Research Council EP/T013265/1 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 01 Sep 2021 10:01 |
Last Modified: | 02 Dec 2022 01:13 |
Published Version: | https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumb... |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Refereed: | Yes |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:177515 |