Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Right for the Right Reason: Making Image Classification Robust   The effectiveness of Convolutional Neural Networks (CNNs)in classifying image
data has been thoroughly demonstrated. In order to explain the classification
to humans, methods for visualizing classification evidence have been developed
in recent years. These explanations reveal that sometimes images are classified
correctly, but for the wrong reasons,i.e., based on incidental evidence. Of
course, it is desirable that images are classified correctly for the right
reasons, i.e., based on the actual evidence. To this end, we propose a new
explanation quality metric to measure object aligned explanation in image
classification which we refer to as theObAlExmetric. Using object detection
approaches, explanation approaches, and ObAlEx, we quantify the focus of CNNs
on the actual evidence. Moreover, we show that additional training of the CNNs
can improve the focus of CNNs without decreasing their accuracy.

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The categories of this paper are: funct-an
30
The categories of this paper are: q-alg
8
The categories of this paper are: hep-ex
27
The categories of this paper are: comp-gas
18
The categories of this paper are: econ
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
21
The categories of this paper are: adap-org
15
The categories of this paper are: nucl-ex
14
The categories of this paper are: math-ph
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
13
The categories of this paper are: quant-ph
0
The categories of this paper are: hep-ph
9
The categories of this paper are: nlin