Search Results for author: Huilin Yin

Found 5 papers, 3 papers with code

PICNN: A Pathway towards Interpretable Convolutional Neural Networks

1 code implementation19 Dec 2023 Wengang Guo, Jiayi Yang, Huilin Yin, Qijun Chen, Wei Ye

Experimental results have demonstrated that our method PICNN (the combination of standard CNNs with our proposed pathway) exhibits greater interpretability than standard CNNs while achieving higher or comparable discrimination power.

Causal Information Bottleneck Boosts Adversarial Robustness of Deep Neural Network

no code implementations25 Oct 2022 Huan Hua, Jun Yan, Xi Fang, Weiquan Huang, Huilin Yin, Wancheng Ge

With the utilization of such a framework, the influence of non-robust features could be mitigated to strengthen the adversarial robustness.

Adversarial Robustness Causal Inference

Wavelet Regularization Benefits Adversarial Training

1 code implementation8 Jun 2022 Jun Yan, Huilin Yin, Xiaoyang Deng, Ziming Zhao, Wancheng Ge, Hao Zhang, Gerhard Rigoll

Since adversarial vulnerability can be regarded as a high-frequency phenomenon, it is essential to regulate the adversarially-trained neural network models in the frequency domain.

Adversarial Robustness

Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic

no code implementations11 Nov 2021 Wei Zhou, Dong Chen, Jun Yan, Zhaojian Li, Huilin Yin, Wanchen Ge

In this paper, we formulate the lane-changing decision making of multiple AVs in a mixed-traffic highway environment as a multi-agent reinforcement learning (MARL) problem, where each AV makes lane-changing decisions based on the motions of both neighboring AVs and HDVs.

Autonomous Driving Decision Making +3

On Procedural Adversarial Noise Attack And Defense

1 code implementation10 Aug 2021 Jun Yan, Xiaoyang Deng, Huilin Yin, Wancheng Ge

Deep Neural Networks (DNNs) are vulnerable to adversarial examples which would inveigle neural networks to make prediction errors with small perturbations on the input images.

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