Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: MetaMix: Improved Meta-Learning with Interpolation-based Consistency
  Regularization   Model-Agnostic Meta-Learning (MAML) and its variants are popular few-shot
classification methods. They train an initializer across a variety of sampled
learning tasks (also known as episodes) such that the initialized model can
adapt quickly to new tasks. However, current MAML-based algorithms have
limitations in forming generalizable decision boundaries. In this paper, we
propose an approach called MetaMix. It generates virtual feature-target pairs
within each episode to regularize the backbone models. MetaMix can be
integrated with any of the MAML-based algorithms and learn the decision
boundaries generalizing better to new tasks. Experiments on the mini-ImageNet,
CUB, and FC100 datasets show that MetaMix improves the performance of
MAML-based algorithms and achieves state-of-the-art result when integrated with
Meta-Transfer Learning.

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The categories of this paper are: math
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The categories of this paper are: q-bio
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The categories of this paper are: nucl-ex
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The categories of this paper are: astro-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: acc-phys
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The categories of this paper are: cond-mat
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The categories of this paper are: patt-sol
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The categories of this paper are: econ
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The categories of this paper are: hep-th
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The categories of this paper are: math-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-ph
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The categories of this paper are: comp-gas
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The categories of this paper are: nlin