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...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	2020 35th IEEE/ACM International Conference on Automated Software Engineering (ASE) S. 410 - 422
Hauptverfasser:	Shen, Weijun, Li, Yanhui, Chen, Lin, Han, Yuanlei, Zhou, Yuming, Xu, Baowen
Format:	Tagungsbericht
Sprache:	Englisch
Veröffentlicht:	ACM 01.09.2020
Schlagworte:	Context modeling Data models Deep learning Labeling Multiple-Boundary Neural network Retraining Software testing Task analysis Testing Training Training data
ISSN:	2643-1572
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!