Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Semantic Network Interpretation   Network interpretation as an effort to reveal the features learned by a
network remains largely visualization-based. In this paper, our goal is to
tackle semantic network interpretation at both filter and decision level. For
filter-level interpretation, we represent the concepts a filter encodes with a
probability distribution of visual attributes. The decision-level
interpretation is achieved by textual summarization that generates an
explanatory sentence containing clues behind a network's decision. A Bayesian
inference algorithm is proposed to automatically associate filters and network
decisions with visual attributes. Human study confirms that the semantic
interpretation is a beneficial alternative or complement to visualization
methods. We demonstrate the crucial role that semantic network interpretation
can play in understanding a network's failure patterns. More importantly,
semantic network interpretation enables a better understanding of the
correlation between a model's performance and its distribution metrics like
filter selectivity and concept sparseness.

34
The categories of this paper are: chem-ph
33
The categories of this paper are: plasm-ph
16
The categories of this paper are: stat
3
The categories of this paper are: physics
21
The categories of this paper are: adap-org
25
The categories of this paper are: patt-sol
6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
26
The categories of this paper are: bayes-an
24
The categories of this paper are: dg-ga
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th