Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Instance Credibility Inference for Few-Shot Learning   Few-shot learning (FSL) aims to recognize new objects with extremely limited
training data for each category. Previous efforts are made by either leveraging
meta-learning paradigm or novel principles in data augmentation to alleviate
this extremely data-scarce problem. In contrast, this paper presents a simple
statistical approach, dubbed Instance Credibility Inference (ICI) to exploit
the distribution support of unlabeled instances for few-shot learning.
Specifically, we first train a linear classifier with the labeled few-shot
examples and use it to infer the pseudo-labels for the unlabeled data. To
measure the credibility of each pseudo-labeled instance, we then propose to
solve another linear regression hypothesis by increasing the sparsity of the
incidental parameters and rank the pseudo-labeled instances with their sparsity
degree. We select the most trustworthy pseudo-labeled instances alongside the
labeled examples to re-train the linear classifier. This process is iterated
until all the unlabeled samples are included in the expanded training set, i.e.
the pseudo-label is converged for unlabeled data pool. Extensive experiments
under two few-shot settings show that our simple approach can establish new
state-of-the-arts on four widely used few-shot learning benchmark datasets
including miniImageNet, tieredImageNet, CIFAR-FS, and CUB. Our code is
available at: https://github.com/Yikai-Wang/ICI-FSL

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The categories of this paper are: alg-geom
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The categories of this paper are: bayes-an
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The categories of this paper are: mtrl-th
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The categories of this paper are: cmp-lg
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The categories of this paper are: patt-sol
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The categories of this paper are: ao-sci
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The categories of this paper are: physics
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The categories of this paper are: cond-mat
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The categories of this paper are: math-ph
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The categories of this paper are: nucl-ex
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The categories of this paper are: comp-gas
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The categories of this paper are: nlin
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The categories of this paper are: math
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The categories of this paper are: eess
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The categories of this paper are: hep-th
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The categories of this paper are: gr-qc