33 citations · 439 across the 68 of their papers we have counts for
12 papers · 1 filter
Learning to Prevent Leakage: Privacy-Preserving Inference in the Mobile Cloud
Shuang Zhang, Liyao Xiang, Congcong Li +4
Powered by machine learning services in the cloud, numerous learning-driven mobile applications are gaining popularity in the market. As deep learning tasks are mostly computation-…
Towards a Unified Evaluation of Explanation Methods without Ground Truth
Hao Zhang, Jiayi Chen, Haotian Xue +1
This paper proposes a set of criteria to evaluate the objectiveness of explanation methods of neural networks, which is crucial for the development of explainable AI, but it also p…
Verifiability and Predictability: Interpreting Utilities of Network Architectures for Point Cloud Processing
Wen Shen, Zhihua Wei, Shikun Huang +4
In this paper, we diagnose deep neural networks for 3D point cloud processing to explore utilities of different intermediate-layer network architectures. We propose a number of hyp…
3D-Rotation-Equivariant Quaternion Neural Networks
Wen Shen, Binbin Zhang, Shikun Huang +2
This paper proposes a set of rules to revise various neural networks for 3D point cloud processing to rotation-equivariant quaternion neural networks (REQNNs). We find that when a…
Knowledge Consistency between Neural Networks and Beyond
Ruofan Liang, Tianlin Li, Longfei Li +2
This paper aims to analyze knowledge consistency between pre-trained deep neural networks. We propose a generic definition for knowledge consistency between neural networks at diff…
Quantification and Analysis of Layer-wise and Pixel-wise Information Discarding
Haotian Ma, Hao Zhang, Fan Zhou +2
This paper presents a method to explain how the information of each input variable is gradually discarded during the forward propagation in a deep neural network (DNN), which provi…
Unsupervised Learning of Neural Networks to Explain Neural Networks (extended abstract)
Quanshi Zhang, Yu Yang, Ying Nian Wu
This paper presents an unsupervised method to learn a neural network, namely an explainer, to interpret a pre-trained convolutional neural network (CNN), i.e., the explainer uses i…
Network Transplanting (extended abstract)
Quanshi Zhang, Yu Yang, Qian Yu +1
This paper focuses on a new task, i.e., transplanting a category-and-task-specific neural network to a generic, modular network without strong supervision. We design a functionally…
Explaining AlphaGo: Interpreting Contextual Effects in Neural Networks
Zenan Ling, Haotian Ma, Yu Yang +3
In this paper, we propose to disentangle and interpret contextual effects that are encoded in a pre-trained deep neural network. We use our method to explain the gaming strategy of…
Interpretable Complex-Valued Neural Networks for Privacy Protection
Liyao Xiang, Haotian Ma, Hao Zhang +3
Previous studies have found that an adversary attacker can often infer unintended input information from intermediate-layer features. We study the possibility of preventing such ad…
Proceedings of AAAI 2019 Workshop on Network Interpretability for Deep Learning
Quanshi Zhang, Lixin Fan, Bolei Zhou
This is the Proceedings of AAAI 2019 Workshop on Network Interpretability for Deep Learning
Interpretable CNNs for Object Classification
Quanshi Zhang, Xin Wang, Ying Nian Wu +2
This paper proposes a generic method to learn interpretable convolutional filters in a deep convolutional neural network (CNN) for object classification, where each interpretable f…