33 citations · 174 across the 39 of their papers we have counts for
20 papers · 1 filter
Interpreting Representation Quality of DNNs for 3D Point Cloud Processing
Wen Shen, Qihan Ren, Dongrui Liu +1
In this paper, we evaluate the quality of knowledge representations encoded in deep neural networks (DNNs) for 3D point cloud processing. We propose a method to disentangle the ove…
Visualizing the Emergence of Intermediate Visual Patterns in DNNs
Mingjie Li, Shaobo Wang, Quanshi Zhang
This paper proposes a method to visualize the discrimination power of intermediate-layer visual patterns encoded by a DNN. Specifically, we visualize (1) how the DNN gradually lear…
Interpretable Compositional Convolutional Neural Networks
Wen Shen, Zhihua Wei, Shikun Huang +4
The reasonable definition of semantic interpretability presents the core challenge in explainable AI. This paper proposes a method to modify a traditional convolutional neural netw…
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…
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…