An Experience Report on Regression-Free Repair of Deep Neural Network Model
Author:
Affiliation:
1. Fujitsu Limited,Kawasaki,Japan
2. National Institute of Informatics,Tokyo,Japan
Funder
JST-Mirai Program
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10123438/10123439/10123626.pdf?arnumber=10123626
Reference11 articles.
1. An Empirical Analysis of Backward Compatibility in Machine Learning Systems
2. NeuRecover: Regression-Controlled Repair of Deep Neural Networks with Training History
3. Positive-Congruent Training: Towards Regression-Free Model Updates
4. Deeprepair: Style-guided repairing for deep neural networks in the real-world operational environment;yu;IEEE Transactions on Reliability,2021
5. Fuzz testing based data augmentation to improve robustness of deep neural networks
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1. More is Not Always Better: Exploring Early Repair of DNNs;Proceedings of the 5th IEEE/ACM International Workshop on Deep Learning for Testing and Testing for Deep Learning;2024-04-20
2. Towards Integrated Model-Based Machine Learning Experimentation Framework;2023 10th International Conference on Dependable Systems and Their Applications (DSA);2023-08-10
3. ArchRepair : Block-Level Architecture-Oriented Repairing for Deep Neural Networks;ACM Transactions on Software Engineering and Methodology;2023-07-24
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