Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Selecting Relevant Features from a Multi-domain Representation for
  Few-shot Classification   Popular approaches for few-shot classification consist of first learning a
generic data representation based on a large annotated dataset, before adapting
the representation to new classes given only a few labeled samples. In this
work, we propose a new strategy based on feature selection, which is both
simpler and more effective than previous feature adaptation approaches. First,
we obtain a multi-domain representation by training a set of semantically
different feature extractors. Then, given a few-shot learning task, we use our
multi-domain feature bank to automatically select the most relevant
representations. We show that a simple non-parametric classifier built on top
of such features produces high accuracy and generalizes to domains never seen
during training, which leads to state-of-the-art results on MetaDataset and
improved accuracy on mini-ImageNet.

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The categories of this paper are: cmp-lg
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The categories of this paper are: ao-sci
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The categories of this paper are: adap-org
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The categories of this paper are: nlin
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The categories of this paper are: physics
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The categories of this paper are: hep-th
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The categories of this paper are: hep-ex
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The categories of this paper are: gr-qc
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The categories of this paper are: math
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The categories of this paper are: patt-sol
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The categories of this paper are: plasm-ph
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The categories of this paper are: chem-ph
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The categories of this paper are: q-fin
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The categories of this paper are: hep-lat
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The categories of this paper are: bayes-an
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The categories of this paper are: solv-int