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Robustness guarantees for deep neural networks on videos

Abstract:

The widespread adoption of deep learning models places demands on their robustness. In this paper, we consider the robustness of deep neural networks on videos, which comprise both the spatial features of individual frames extracted by a convolutional neural network and the temporal dynamics between adjacent frames captured by a recurrent neural network. To measure robustness, we study the maximum safe radius problem, which computes the minimum distance from the optical flow sequence obtained...

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Publication status:
Published
Peer review status:
Peer reviewed

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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
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Institution:
University of Oxford
Division:
MPLS
Department:
Computer Science
Role:
Author
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Name:
European Commission
Grant:
834115
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Name:
Engineering & Physical Sciences Research Council
Grant:
EP/M019918/1
Publisher:
IEEE
Publication date:
2020-08-05
Acceptance date:
2020-02-23
Event title:
2020 Conference on Computer Vision and Pattern Recognition (CVPR 2020)
Event location:
Seattle, Washington, USA
Event website:
http://cvpr2020.thecvf.com/
Event start date:
2020-06-14
Event end date:
2020-06-19
DOI:
Language:
English
Keywords:
Pubs id:
1097549
Local pid:
pubs:1097549
Deposit date:
2020-03-30

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