Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Prototypical Classifier for Robust Class-Imbalanced Learning   Deep neural networks have been shown to be very powerful methods for many
supervised learning tasks. However, they can also easily overfit to training
set biases, i.e., label noise and class imbalance. While both learning with
noisy labels and class-imbalanced learning have received tremendous attention,
existing works mainly focus on one of these two training set biases. To fill
the gap, we propose \textit{Prototypical Classifier}, which does not require
fitting additional parameters given the embedding network. Unlike conventional
classifiers that are biased towards head classes, Prototypical Classifier
produces balanced and comparable predictions for all classes even though the
training set is class-imbalanced. By leveraging this appealing property, we can
easily detect noisy labels by thresholding the confidence scores predicted by
Prototypical Classifier, where the threshold is dynamically adjusted through
the iteration. A sample reweghting strategy is then applied to mitigate the
influence of noisy labels. We test our method on CIFAR-10-LT, CIFAR-100-LT and
Webvision datasets, observing that Prototypical Classifier obtains substaintial
improvements compared with state of the arts.

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The categories of this paper are: dg-ga
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The categories of this paper are: math
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The categories of this paper are: nucl-ex
36
The categories of this paper are: cmp-lg
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The categories of this paper are: comp-gas
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The categories of this paper are: astro-ph
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The categories of this paper are: supr-con
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The categories of this paper are: eess
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The categories of this paper are: econ
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The categories of this paper are: stat
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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: funct-an
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The categories of this paper are: quant-ph
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The categories of this paper are: alg-geom
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The categories of this paper are: gr-qc