Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Are Few-Shot Learning Benchmarks too Simple ? Solving them without Task
  Supervision at Test-Time   We show that several popular few-shot learning benchmarks can be solved with
varying degrees of success without using support set Labels at Test-time (LT).
To this end, we introduce a new baseline called Centroid Networks, a
modification of Prototypical Networks in which the support set labels are
hidden from the method at test-time and have to be recovered through
clustering. A benchmark that can be solved perfectly without LT does not
require proper task adaptation and is therefore inadequate for evaluating
few-shot methods. In practice, most benchmarks cannot be solved perfectly
without LT, but running our baseline on any new combinations of architectures
and datasets gives insights on the baseline performance to be expected from
leveraging a good representation, before any adaptation to the test-time
labels.

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The categories of this paper are: gr-qc
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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: mtrl-th
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The categories of this paper are: math
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The categories of this paper are: hep-ph
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The categories of this paper are: econ
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The categories of this paper are: math-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: q-bio
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The categories of this paper are: quant-ph
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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: hep-lat
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The categories of this paper are: hep-th
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The categories of this paper are: supr-con