Towards a Unified Game-Theoretic View of Adversarial Perturbations and Robustness

1 code implementation • NeurIPS 2021 • Jie Ren, Die Zhang, Yisen Wang, Lu Chen, Zhanpeng Zhou, Yiting Chen, Xu Cheng, Xin Wang, Meng Zhou, Jie Shi, Quanshi Zhang

This paper provides a unified view to explain different adversarial attacks and defense methods, i. e. the view of multi-order interactions between input variables of DNNs.

Adversarial Robustness

A Unified Game-Theoretic Interpretation of Adversarial Robustness

1 code implementation • 5 Nov 2021 • Jie Ren, Die Zhang, Yisen Wang, Lu Chen, Zhanpeng Zhou, Yiting Chen, Xu Cheng, Xin Wang, Meng Zhou, Jie Shi, Quanshi Zhang

This paper provides a unified view to explain different adversarial attacks and defense methods, \emph{i. e.} the view of multi-order interactions between input variables of DNNs.

Adversarial Robustness

A Unified Game-Theoretic Interpretation of Adversarial Robustness

1 code implementation • 12 Mar 2021 • Jie Ren, Die Zhang, Yisen Wang, Lu Chen, Zhanpeng Zhou, Yiting Chen, Xu Cheng, Xin Wang, Meng Zhou, Jie Shi, Quanshi Zhang

This paper provides a unified view to explain different adversarial attacks and defense methods, i. e. the view of multi-order interactions between input variables of DNNs.

Adversarial Robustness

Interpreting Multivariate Shapley Interactions in DNNs

no code implementations • 10 Oct 2020 • Hao Zhang, Yichen Xie, Longjie Zheng, Die Zhang, Quanshi Zhang

In this paper, we define and quantify the significance of interactions among multiple input variables of the DNN.

Building Interpretable Interaction Trees for Deep NLP Models

no code implementations • 29 Jun 2020 • Die Zhang, Huilin Zhou, Hao Zhang, Xiaoyi Bao, Da Huo, Ruizhao Chen, Xu Cheng, Mengyue Wu, Quanshi Zhang

This paper proposes a method to disentangle and quantify interactions among words that are encoded inside a DNN for natural language processing.

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