Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Relating Input Concepts to Convolutional Neural Network Decisions   Many current methods to interpret convolutional neural networks (CNNs) use
visualization techniques and words to highlight concepts of the input seemingly
relevant to a CNN's decision. The methods hypothesize that the recognition of
these concepts are instrumental in the decision a CNN reaches, but the nature
of this relationship has not been well explored. To address this gap, this
paper examines the quality of a concept's recognition by a CNN and the degree
to which the recognitions are associated with CNN decisions. The study
considers a CNN trained for scene recognition over the ADE20k dataset. It uses
a novel approach to find and score the strength of minimally distributed
representations of input concepts (defined by objects in scene images) across
late stage feature maps. Subsequent analysis finds evidence that concept
recognition impacts decision making. Strong recognition of concepts
frequently-occurring in few scenes are indicative of correct decisions, but
recognizing concepts common to many scenes may mislead the network.

31
The categories of this paper are: ao-sci
29
The categories of this paper are: funct-an
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
4
The categories of this paper are: cond-mat
25
The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
12
The categories of this paper are: nucl-th
10
The categories of this paper are: q-bio
3
The categories of this paper are: physics
37
The categories of this paper are: supr-con
28
The categories of this paper are: alg-geom
16
The categories of this paper are: stat
30
The categories of this paper are: q-alg