To what extent do DNN-based image classification models make unreliable inferences?-Reference-Cited by-全球学者库

To what extent do DNN-based image classification models make unreliable inferences?

Published:2021-06-18 Issue:5 Volume:26 Page:
ISSN:1382-3256
Container-title:Empirical Software Engineering
language:en
Short-container-title:Empir Software Eng

Author:

Tian Yongqiang^ORCID,Ma Shiqing,Wen Ming^ORCID,Liu Yepang^ORCID,Cheung Shing-Chi^ORCID,Zhang Xiangyu

Publisher

Springer Science and Business Media LLC

Subject

Software

Link

https://link.springer.com/content/pdf/10.1007/s10664-021-09985-1.pdf

Reference72 articles.

1. Aggarwal A, Lohia P, Nagar S, Dey K, Saha D (2019) Black box fairness testing of machine learning models. In: Proceedings of the 2019 27th ACM joint meeting on european software engineering conference and symposium on the foundations of software engineering, association for computing machinery, ESEC/FSE 2019, New York, NY, USA, pp 625–635. https://doi.org/10.1145/3338906.3338937

2. Barr ET, Harman M, McMinn P, Shahbaz M, Yoo S (2015) The oracle problem in software testing: A survey. IEEE Trans Softw Eng 41 (5):507–525

3. Ben-Baruch E, Ridnik T, Zamir N, Noy A, Friedman I, Protter M, Zelnik-Manor L (2020) Asymmetric loss for multi-label classification. arXiv:2009.14119

4. Benesty J, Chen J, Huang Y, Cohen I (2009) Pearson correlation coefficient. In: Noise reduction in speech processing. Springer, pp 1–4

5. Carlini N, Wagner DA (2017) Towards evaluating the robustness of neural networks. In: 2017 IEEE symposium on security and privacy, SP 2017, May 22-26, 2017. IEEE Computer Society, San Jose, CA, USA, pp 39–57. https://doi.org/10.1109/SP.2017.49

Cited by 14 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. BinAug: Enhancing Binary Similarity Analysis with Low-Cost Input Repairing;Proceedings of the 46th IEEE/ACM International Conference on Software Engineering;2024-02-06

2. Towards Feature-Based Analysis of the Machine Learning Development Lifecycle;Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering;2023-11-30

3. Nuances are the Key: Unlocking ChatGPT to Find Failure-Inducing Tests with Differential Prompting;2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE);2023-09-11

4. ATOM: Automated Black-Box Testing of Multi-Label Image Classification Systems;2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE);2023-09-11

5. Finding Deviated Behaviors of the Compressed DNN Models for Image Classifications;ACM Transactions on Software Engineering and Methodology;2023-07-24