Interpretability based Neural Network Repair
This program is tentative and subject to change.
Along with the prevalent use of Deep neural networks (DNNs), concerns have been raised on the security threats from DNNs such as backdoors in the network. While neural network repair methods have shown to be effective for fixing the defects in DNNs, they have been also found to produce biased models, with imbalanced accuracy across different classes, or weakened adversarial robustness, allowing malicious attackers to trick the model by adding small perturbations. To address these challenges, we propose INNER, an INterpretability-based NEural Repair approach. INNER formulates the idea of \emph{neuron routing} for identifying fault neurons, in which the interpretability technique \emph{model probe} is used to evaluate each neuron’s contribution to the undesired behaviour of the neural network. INNER then optimizes the identified neurons for repairing the neural network. We test INNER on three typical application scenarios, including backdoor attacks, adversarial attacks, and wrong predictions. Our experimental results demonstrate that INNER can effectively repair neural networks, by ensuring accuracy, fairness, and robustness. Moreover, the performance of other repair methods can be also improved by re-using the fault neurons found by INNER, justifying the generality of the proposed approach.
This program is tentative and subject to change.
Wed 18 SepDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
15:30 - 17:10 | |||
15:30 20mTalk | Interoperability in Deep Learning: A User Survey and Failure Analysis of ONNX Model Converters Technical Papers Purvish Jajal Purdue University, Wenxin Jiang Purdue University, Arav Tewari Purdue University, Erik Kocinare Purdue University, Joseph Woo Purdue University, Anusha Sarraf Purdue University, Yung-Hsiang Lu Purdue University, George K. Thiruvathukal Loyola University Chicago and Argonne National Laboratory, James C. Davis Purdue University Pre-print | ||
15:50 20mTalk | Interpretability based Neural Network Repair Technical Papers Zuohui Chen Zhejiang University of Technology, Jun Zhou Zhejiang University of Technology, Youcheng Sun The University of Manchester, Jingyi Wang Zhejiang University, Qi Xuan Zhejiang University of Technology, Xiaoniu Yang Zhejiang University of Technology | ||
16:10 20mTalk | See the Forest, not Trees: Unveiling and Escaping the Pitfalls of Error-Triggering Inputs in Neural Network Testing Technical Papers Yuanyuan Yuan The Hong Kong University of Science and Technology, Shuai Wang The Hong Kong University of Science and Technology, Zhendong Su ETH Zurich | ||
16:30 20mTalk | Isolation-Based Debugging for Neural Networks Technical Papers Jialuo Chen Zhejiang University, Jingyi Wang Zhejiang University, Youcheng Sun The University of Manchester, Peng Cheng Zhejiang University, Jiming Chen Zhejiang University DOI | ||
16:50 20mTalk | Certified Continual Learning for Neural Network Regression Technical Papers Long Pham Hong Singapore Management University, Jun Sun School of Information Systems, Singapore Management University, Singapore |