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20202023
most citedRotation-Equivariant Neural Networks for Privacy Protection

1 citations · 1 across the 3 of their papers we have counts for

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6 papers · 1 filter

cs.LG2023

Going Beyond Neural Network Feature Similarity: The Network Feature Complexity and Its Interpretation Using Category Theory

Yiting Chen, Zhanpeng Zhou, Junchi Yan

The behavior of neural networks still remains opaque, and a recently widely noted phenomenon is that networks often achieve similar performance when initialized with different rand…

cs.LG2021

A Unified Game-Theoretic Interpretation of Adversarial Robustness

Jie Ren, Die Zhang, Yisen Wang +8

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. B…

cs.LG2021

A Unified Game-Theoretic Interpretation of Adversarial Robustness

Jie Ren, Die Zhang, Yisen Wang +8

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. Based on…

cs.LG2020

Technical Note: Game-Theoretic Interactions of Different Orders

Hao Zhang, Xu Cheng, Yiting Chen +1

In this study, we define interaction components of different orders between two input variables based on game theory. We further prove that interaction components of different orde…

cs.LG20201 cited

Rotation-Equivariant Neural Networks for Privacy Protection

Hao Zhang, Yiting Chen, Haotian Ma +5

In order to prevent leaking input information from intermediate-layer features, this paper proposes a method to revise the traditional neural network into the rotation-equivariant…

cs.LG2020

Deep Quaternion Features for Privacy Protection

Hao Zhang, Yiting Chen, Liyao Xiang +3

We propose a method to revise the neural network to construct the quaternion-valued neural network (QNN), in order to prevent intermediate-layer features from leaking input informa…