VeRe: Verification Guided Synthesis for Repairing Deep Neural Networks-Reference-Cited by-同舟云学术

VeRe: Verification Guided Synthesis for Repairing Deep Neural Networks

Published:2024-02-06 Issue: Volume:107 Page:1-13
ISSN:
Container-title:Proceedings of the IEEE/ACM 46th International Conference on Software Engineering
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Author:

Ma Jianan¹²^ORCID,Yang Pengfei³^ORCID,Wang Jingyi²⁴^ORCID,Sun Youcheng⁵^ORCID,Huang Cheng-Chao⁶^ORCID,Wang Zhen¹^ORCID

Affiliation:

1. Hangzhou Dianzi University, Hangzhou, China

2. Zhejiang University, Hangzhou, China

3. SKLCS, Institute of Software, CAS, Beijing, China

4. ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou, China

5. University of Manchester, Manchester, United Kingdom

6. Nanjing Institute of Software Technology, ISCAS, Nanjing, China

Funder

the Key R&D Programs of Zhejiang

the National Natural Science Foundation of China

the CAS Project for Young Scientists in Basic Research

the Joint Funds of National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3597503.3623332

Reference89 articles.

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2. Application of deep learning for retinal image analysis: A review

3. Teodora Baluta, Zheng Leong Chua, Kuldeep S Meel, and Prateek Saxena. 2021. Scalable quantitative verification for deep neural networks. In ICSE 2021. IEEE, Madrid, Spain, 312--323.

4. Teodora Baluta, Shiqi Shen, Shweta Shinde, Kuldeep S. Meel, and Prateek Saxena. 2019. Quantitative Verification of Neural Networks and Its Security Applications. In CCS 2019, November 11--15, 2019. ACM, London, UK, 1249--1264.

5. Ben Batten Panagiotis Kouvaros Alessio Lomuscio and Yang Zheng. 2021. Efficient neural network verification via layer-based semidefinite relaxations and linear cuts. IJCAI.

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