Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Interpreting Representation Quality of DNNs for 3D Point Cloud
  Processing   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.

31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
8
The categories of this paper are: hep-ex
12
The categories of this paper are: nucl-th
21
The categories of this paper are: adap-org
3
The categories of this paper are: physics
35
The categories of this paper are: mtrl-th
15
The categories of this paper are: nucl-ex
18
The categories of this paper are: econ
36
The categories of this paper are: cmp-lg
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph
37
The categories of this paper are: supr-con
24
The categories of this paper are: dg-ga
0
The categories of this paper are: hep-ph