Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning with Noisy Labels by Targeted Relabeling   Crowdsourcing platforms are often used to collect datasets for training deep
neural networks, despite higher levels of inaccurate labeling compared to
expert labeling. There are two common strategies to manage the impact of this
noise, the first involves aggregating redundant annotations, but comes at the
expense of labeling substantially fewer examples. Secondly, prior works have
also considered using the entire annotation budget to label as many examples as
possible and subsequently apply denoising algorithms to implicitly clean up the
dataset. We propose an approach which instead reserves a fraction of
annotations to explicitly relabel highly probable labeling errors. In
particular, we allocate a large portion of the labeling budget to form an
initial dataset used to train a model. This model is then used to identify
specific examples that appear most likely to be incorrect, which we spend the
remaining budget to relabel. Experiments across three model variations and four
natural language processing tasks show our approach outperforms both label
aggregation and advanced denoising methods designed to handle noisy labels when
allocated the same annotation budget.

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The categories of this paper are: hep-ex
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The categories of this paper are: stat
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The categories of this paper are: alg-geom
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The categories of this paper are: quant-ph
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The categories of this paper are: bayes-an
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The categories of this paper are: econ
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The categories of this paper are: nucl-th
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The categories of this paper are: math
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The categories of this paper are: chao-dyn
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The categories of this paper are: plasm-ph
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The categories of this paper are: ao-sci
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The categories of this paper are: cond-mat
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The categories of this paper are: gr-qc
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The categories of this paper are: solv-int
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The categories of this paper are: mtrl-th
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The categories of this paper are: acc-phys