Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Interpretation of Feature Space using Multi-Channel Attentional
  Sub-Networks   Convolutional Neural Networks have achieved impressive results in various
tasks, but interpreting the internal mechanism is a challenging problem. To
tackle this problem, we exploit a multi-channel attention mechanism in feature
space. Our network architecture allows us to obtain an attention mask for each
feature while existing CNN visualization methods provide only a common
attention mask for all features. We apply the proposed multi-channel attention
mechanism to multi-attribute recognition task. We can obtain different
attention mask for each feature and for each attribute. Those analyses give us
deeper insight into the feature space of CNNs. The experimental results for the
benchmark dataset show that the proposed method gives high interpretability to
humans while accurately grasping the attributes of the data.

34
The categories of this paper are: chem-ph
12
The categories of this paper are: nucl-th
3
The categories of this paper are: physics
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
9
The categories of this paper are: nlin
11
The categories of this paper are: hep-lat
2
The categories of this paper are: math
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th