Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Improving Accuracy of Nonparametric Transfer Learning via Vector
  Segmentation   Transfer learning using deep neural networks as feature extractors has become
increasingly popular over the past few years. It allows to obtain
state-of-the-art accuracy on datasets too small to train a deep neural network
on its own, and it provides cutting edge descriptors that, combined with
nonparametric learning methods, allow rapid and flexible deployment of
performing solutions in computationally restricted settings. In this paper, we
are interested in showing that the features extracted using deep neural
networks have specific properties which can be used to improve accuracy of
downstream nonparametric learning methods. Namely, we demonstrate that for some
distributions where information is embedded in a few coordinates, segmenting
feature vectors can lead to better accuracy. We show how this model can be
applied to real datasets by performing experiments using three mainstream deep
neural network feature extractors and four databases, in vision and audio.

10
The categories of this paper are: q-bio
36
The categories of this paper are: cmp-lg
24
The categories of this paper are: dg-ga
6
The categories of this paper are: astro-ph
31
The categories of this paper are: ao-sci
2
The categories of this paper are: math
8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
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The categories of this paper are: nucl-ex
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The categories of this paper are: funct-an
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The categories of this paper are: solv-int
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The categories of this paper are: cond-mat
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The categories of this paper are: hep-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: adap-org
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The categories of this paper are: eess