Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Collaborative Label Correction via Entropy Thresholding   Deep neural networks (DNNs) have the capacity to fit extremely noisy labels
nonetheless they tend to learn data with clean labels first and then memorize
those with noisy labels. We examine this behavior in light of the Shannon
entropy of the predictions and demonstrate the low entropy predictions
determined by a given threshold are much more reliable as the supervision than
the original noisy labels. It also shows the advantage in maintaining more
training samples than previous methods. Then, we power this entropy criterion
with the Collaborative Label Correction (CLC) framework to further avoid
undesired local minimums of the single network. A range of experiments have
been conducted on multiple benchmarks with both synthetic and real-world
settings. Extensive results indicate that our CLC outperforms several
state-of-the-art methods.

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The categories of this paper are: q-alg
31
The categories of this paper are: ao-sci
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The categories of this paper are: q-fin
23
The categories of this paper are: solv-int
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
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The categories of this paper are: adap-org
34
The categories of this paper are: chem-ph
10
The categories of this paper are: q-bio
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The categories of this paper are: dg-ga
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The categories of this paper are: alg-geom
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The categories of this paper are: atom-ph
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The categories of this paper are: eess
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The categories of this paper are: comp-gas
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics