Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Gradient Weighted Superpixels for Interpretability in CNNs   As Convolutional Neural Networks embed themselves into our everyday lives,
the need for them to be interpretable increases. However, there is often a
trade-off between methods that are efficient to compute but produce an
explanation that is difficult to interpret, and those that are slow to compute
but provide a more interpretable result. This is particularly challenging in
problem spaces that require a large input volume, especially video which
combines both spatial and temporal dimensions. In this work we introduce the
idea of scoring superpixels through the use of gradient based pixel scoring
techniques. We show qualitatively and quantitatively that this is able to
approximate LIME, in a fraction of the time. We investigate our techniques
using both image classification, and action recognition networks on large scale
datasets (ImageNet and Kinetics-400 respectively).

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The categories of this paper are: math
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The categories of this paper are: math-ph
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The categories of this paper are: nlin
30
The categories of this paper are: q-alg
29
The categories of this paper are: funct-an
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The categories of this paper are: comp-gas
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The categories of this paper are: cmp-lg
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The categories of this paper are: adap-org
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The categories of this paper are: bayes-an
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The categories of this paper are: astro-ph
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The categories of this paper are: gr-qc
37
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: q-fin
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The categories of this paper are: alg-geom
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The categories of this paper are: cond-mat