Interpreting Representation Quality of DNNs for 3D Point Cloud ProcessingDownload PDF

21 May 2021, 20:42 (edited 22 Jan 2022)NeurIPS 2021 PosterReaders: Everyone
  • Keywords: 3D point cloud processing, Explainable AI
  • TL;DR: We evaluate the quality of knowledge representations encoded in deep neural networks for 3D point cloud processing.
  • Abstract: 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 overall model vulnerability into the sensitivity to the rotation, the translation, the scale, and local 3D structures. Besides, we also propose metrics to evaluate the spatial smoothness of encoding 3D structures, and the representation complexity of the DNN. Based on such analysis, experiments expose representation problems with classic DNNs, and explain the utility of the adversarial training. The code will be released when this paper is accepted.
  • Supplementary Material: pdf
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  • Code: https://github.com/ada-shen/Interpret_quality
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