Accelerating constraint-based neural network repairs by example prioritization and selection

Graphical abstract

References

[1] Sotoudeh M, Thakur A V. Provable repair of deep neural networks. In: Proceedings of the 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation. 2021, 588 −603

[2] Sun S, Yan J, Yan R. Layer-specific repair of neural network classifiers. In: Proceedings of the 31st International Conference on Artificial Neural Networks and Machine Learning. 2022, 550−561

[3] Sohn J, Kang S, Yoo S . Arachne: search-based repair of deep neural networks. ACM Transactions on Software Engineering and Methodology, 2023, 32( 4): 85

[4] Qi H, Wang Z, Guo Q, Chen J, Juefei-Xu F, Zhang F, Ma L, Zhao J . ArchRepair: block-level architecture-oriented repairing for deep neural networks. ACM Transactions on Software Engineering and Methodology, 2023, 32( 5): 129

[5] Li T, Xie X, Wang J, Guo Q, Liu A, Ma L, Liu Y . Faire: repairing fairness of neural networks via neuron condition synthesis. ACM Transactions on Software Engineering and Methodology, 2024, 33( 1): 21

[6] Tao Z, Nawas S, Mitchell J, Thakur A V . Architecture-preserving provable repair of deep neural networks. Proceedings of the ACM on Programming Languages, 2023, 7( PLDI): 124

[7] Jiang J, Yang J, Zhang Y, Wang Z, You H, Chen J . A post-training framework for improving the performance of deep learning models via model transformation. ACM Transactions on Software Engineering and Methodology, 2024, 33( 3): 61

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grant No. 62132020), and the Major Project of ISCAS (ISCAS-ZD-202302).

Competing interests

The authors declare that they have no competing interests or financial conflicts to disclose.

RIGHTS & PERMISSIONS

2025 Higher Education Press