Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Visual Understanding of Multiple Attributes Learning Model of X-Ray
  Scattering Images   This extended abstract presents a visualization system, which is designed for
domain scientists to visually understand their deep learning model of
extracting multiple attributes in x-ray scattering images. The system focuses
on studying the model behaviors related to multiple structural attributes. It
allows users to explore the images in the feature space, the classification
output of different attributes, with respect to the actual attributes labelled
by domain scientists. Abundant interactions allow users to flexibly select
instance images, their clusters, and compare them visually in details. Two
preliminary case studies demonstrate its functionalities and usefulness.

22
The categories of this paper are: chao-dyn
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
26
The categories of this paper are: bayes-an
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
25
The categories of this paper are: patt-sol
29
The categories of this paper are: funct-an
27
The categories of this paper are: comp-gas