Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Bridging the Gap: Simultaneous Fine Tuning for Data Re-Balancing   There are many real-world classification problems wherein the issue of data
imbalance (the case when a data set contains substantially more samples for
one/many classes than the rest) is unavoidable. While under-sampling the
problematic classes is a common solution, this is not a compelling option when
the large data class is itself diverse and/or the limited data class is
especially small. We suggest a strategy based on recent work concerning limited
data problems which utilizes a supplemental set of images with similar
properties to the limited data class to aid in the training of a neural
network. We show results for our model against other typical methods on a
real-world synthetic aperture sonar data set. Code can be found at
github.com/JohnMcKay/dataImbalance.

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The categories of this paper are: hep-th
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The categories of this paper are: q-bio
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The categories of this paper are: math
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The categories of this paper are: physics
29
The categories of this paper are: funct-an
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The categories of this paper are: patt-sol
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The categories of this paper are: adap-org
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The categories of this paper are: econ
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The categories of this paper are: q-alg
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The categories of this paper are: nlin
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The categories of this paper are: gr-qc
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The categories of this paper are: quant-ph
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The categories of this paper are: acc-phys
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The categories of this paper are: stat
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The categories of this paper are: comp-gas
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The categories of this paper are: mtrl-th