ArchRepair: Block-Level Architecture-Oriented Repairing for Deep Neural Networks

February 21, 2023


Over the past few years, deep neural networks (DNNs) have achieved tremendous success and have been continuously applied in many application domains. However, during the practical deployment in industrial tasks, DNNs are found to be erroneous-prone due to various reasons such as overfitting and lacking of robustness to real-world corruptions during practical usage. To address these challenges, many recent attempts have been made to repair DNNs for version updates under practical operational contexts by updating weights (i.e., network parameters) through retraining, fine-tuning, or direct weight fixing at a neural level. Nevertheless, existing solutions often neglect the effects of neural network architecture and weight relationships across neurons and layers. In this work, as the first attempt, we initiate to repair DNNs by jointly optimizing the architecture and weights at a higher (i.e., block) level. We first perform empirical studies to investigate the limitation of whole network-level and layer-level repairing, which motivates us to explore a novel repairing direction for DNN repair at the block level. To this end, we need to further consider techniques to address two key technical challenges, i.e., block localization, where we should localize the targeted block thatwe need to fix; and howto perform joint architecture and weight repairing. Specifically, we first propose adversarial-aware spectrum analysis for vulnerable block localization that considers the neurons’ status and weights’ gradients in blocks during the forward and backward processes, which enables more accurate candidate block localization for repairing even under a few examples. Then, we further propose the architecture-oriented search-based repairing that relaxes the targeted block to a continuous repairing search space at higher deep feature levels. By jointly optimizing the architecture and weights in that space, we can identify a much better block architecture. We implement our proposed repairing techniques as a tool, named ArchRepair, and conduct extensive experiments to validate the proposed method. The results show that our method can not only repair but also enhance accuracy & robustness, outperforming the state-of-the-art DNN repair techniques.

Download the Paper


Written by

Felix Xu

Fuyuan Zhang

Hua Qi

Jianjun Zhao

Jianlang Chen

Lei Ma

Qing Guo

Zhijie Wang


ACM Transactions on Software Engineering and Methodology (TOSEM)

Research Topics

Computer Vision

Core Machine Learning

Related Publications

May 09, 2023


ImageBind: One Embedding Space To Bind Them All

Rohit Girdhar, Alaa El-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, Ishan Misra

May 09, 2023

May 04, 2023



MoDem: Accelerating Visual Model-Based Reinforcement Learning with Demonstrations

Nicklas Hansen, Yixin Lin, Hao Su, Xiaolong Wang, Vikash Kumar, Aravind Rajeswaran

May 04, 2023

May 01, 2023



Meta-Learning in Games

Keegan Harris, Ioannis Anagnostides, Gabriele Farina, Mikhail Khodak, Zhiwei Steven Wu, Tuomas Sandholm, Maria-Florina Balcan

May 01, 2023

April 26, 2023



Green Federated Learning

Ashkan Yousefpour, Shen Guo, Ashish Shenoy, Sayan Ghosh, Pierre Stock, Kiwan Maeng, Schalk Krüger, Mike Rabbat, Carole-Jean Wu, Ilya Mironov

April 26, 2023

Help Us Pioneer The Future of AI

We share our open source frameworks, tools, libraries, and models for everything from research exploration to large-scale production deployment.