Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generalized Jensen-Shannon Divergence Loss for Learning with Noisy
  Labels   Prior works have found it beneficial to combine provably noise-robust loss
functions e.g., mean absolute error (MAE) with standard categorical loss
function e.g. cross entropy (CE) to improve their learnability. Here, we
propose to use Jensen-Shannon divergence as a noise-robust loss function and
show that it interestingly interpolate between CE and MAE with a controllable
mixing parameter. Furthermore, we make a crucial observation that CE exhibit
lower consistency around noisy data points. Based on this observation, we adopt
a generalized version of the Jensen-Shannon divergence for multiple
distributions to encourage consistency around data points. Using this loss
function, we show state-of-the-art results on both synthetic (CIFAR), and
real-world (e.g., WebVision) noise with varying noise rates.

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The categories of this paper are: cond-mat
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The categories of this paper are: gr-qc
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: solv-int
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The categories of this paper are: cmp-lg
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The categories of this paper are: mtrl-th
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The categories of this paper are: chem-ph
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The categories of this paper are: adap-org
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The categories of this paper are: quant-ph
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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: ao-sci
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The categories of this paper are: hep-ph
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The categories of this paper are: dg-ga
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The categories of this paper are: atom-ph