Multiple-Boundary Clustering and Prioritization to Promote Neural Network Retraining

With the increasing application of deep learning (DL) models in many safety-critical scenarios, effective and efficient DL testing techniques are much in demand to improve the quality of DL models. One of the major challenges is the data gap between the training data to construct the models and the...

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Bibliographic Details

Published in:	2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) pp. 410 - 422
Main Authors:	Shen, Weijun, Li, Yanhui, Chen, Lin, Han, Yuanlei, Zhou, Yuming, Xu, Baowen
Format:	Conference Proceeding
Language:	English
Published:	ACM 01.09.2020
Subjects:	Context modeling Data models Deep learning Labeling Multiple-Boundary Neural network Retraining Software testing Task analysis Testing Training Training data
ISSN:	2643-1572
Online Access:	Get full text
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