Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ClassSim: Similarity between Classes Defined by Misclassification Ratios
  of Trained Classifiers   Deep neural networks (DNNs) have achieved exceptional performances in many
tasks, particularly, in supervised classification tasks. However, achievements
with supervised classification tasks are based on large datasets with
well-separated classes. Typically, real-world applications involve wild
datasets that include similar classes; thus, evaluating similarities between
classes and understanding relations among classes are important. To address
this issue, a similarity metric, ClassSim, based on the misclassification
ratios of trained DNNs is proposed herein. We conducted image recognition
experiments to demonstrate that the proposed method provides better
similarities compared with existing methods and is useful for classification
problems. Source code including all experimental results is available at
https://github.com/karino2/ClassSim/.

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The categories of this paper are: supr-con
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The categories of this paper are: hep-ex
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The categories of this paper are: hep-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: nucl-ex
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The categories of this paper are: hep-th
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The categories of this paper are: adap-org
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The categories of this paper are: eess
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The categories of this paper are: q-fin
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The categories of this paper are: mtrl-th
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The categories of this paper are: acc-phys
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The categories of this paper are: atom-ph
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The categories of this paper are: math
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The categories of this paper are: alg-geom
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The categories of this paper are: cmp-lg
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The categories of this paper are: plasm-ph