Reference : HUDD: A tool to debug DNNs for safety analysis
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Security, Reliability and Trust
http://hdl.handle.net/10993/50137
HUDD: A tool to debug DNNs for safety analysis
English
Fahmy, Hazem mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SVV >]
Pastore, Fabrizio mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SVV >]
Briand, Lionel mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SVV >]
May-2022
2022 IEEE/ACM 44st International Conference on Software Engineering
Yes
International
International Conference on Software Engineering
from 22-05-2022 to 27-05-2022
[en] DNN Explanation ; Debugging ; Heatmaps ; AI ; DNN Functional Safety Analysis
[en] We present HUDD, a tool that supports safety analysis practices for systems enabled by Deep Neural Networks (DNNs) by automatically identifying the root causes for DNN errors and retraining the DNN. HUDD stands for Heatmap-based Unsupervised Debugging of DNNs, it automatically clusters error-inducing images whose results are due to common subsets of DNN neurons. The intent is for the generated clusters to group error-inducing images having common characteristics, that is, having a common root cause.
HUDD identifies root causes by applying a clustering algorithm to matrices (i.e., heatmaps) capturing the relevance of every DNN neuron on the DNN outcome. Also, HUDD retrains DNNs with images that are automatically selected based on their relatedness to the identified image clusters. Our empirical evaluation with DNNs from the automotive domain have shown that HUDD automatically identifies all the distinct root causes of DNN errors, thus supporting safety analysis. Also, our retraining approach has shown to be more effective at improving DNN accuracy than existing approaches. A demo video of HUDD is available at https://youtu.be/drjVakP7jdU.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Software Verification and Validation Lab (SVV Lab)
Fonds National de la Recherche - FnR ; European Commission - EC
BRIDGES2020/IS/14711346/FUNTASY
Researchers ; Professionals
http://hdl.handle.net/10993/50137
https://youtu.be/drjVakP7jdU
H2020 ; 694277 - TUNE - Testing the Untestable: Model Testing of Complex Software-Intensive Systems
FnR ; FNR14711346 > Fabrizio Pastore > FUNTASY > Functional Safety For Autonomous Systems > 01/08/2020 > 31/07/2023 > 2020

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