Synthetic Data Generation Using Combinatorial Testing and Variational Autoencoder-Reference-Cited by-同舟云学术

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Synthetic Data Generation Using Combinatorial Testing and Variational Autoencoder

Published:2023-04 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)
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Author:

Khadka Krishna¹,Chandrasekaran Jaganmohan²,Lei Yu¹,Kacker Raghu N.³,Richard Kuhn D.³

Affiliation:

1. The University of Texas at Arlington,Department of Computer Science and Engineering,Arlington,Texas,USA,76019

2. National Security Institute,Virginia Tech,Arlington,Virginia,USA,22203

3. National Institute of Standards and Technology,Information Technology Laboratory,Gaithersburg,Maryland,USA,20899

Funder

National Institute of Standards and Technology

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10132157/10132158/10132195.pdf?arnumber=10132195

Reference32 articles.

1. IPOG: A General Strategy for T-Way Software Testing

2. Generating privacy-preserving synthetic tabular data using oblivious variational autoencoders;vardhan;Proceedings of the Workshop on Economics of Privacy and Data Labor at the 37 th International Conference on Machine Learning (ICML),2020

3. The Synthetic Data Vault

4. Scikit-learn: Machine learning in Python;v;The Journal of Machine Learning Research,2011

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1. Enhancement of GUI Display Error Detection Using Improved Faster R-CNN and Multi-Scale Attention Mechanism;Applied Sciences;2024-01-30

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