Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: A Simple yet Effective Baseline for Robust Deep Learning with Noisy
  Labels   Recently deep neural networks have shown their capacity to memorize training
data, even with noisy labels, which hurts generalization performance. To
mitigate this issue, we provide a simple but effective baseline method that is
robust to noisy labels, even with severe noise. Our objective involves a
variance regularization term that implicitly penalizes the Jacobian norm of the
neural network on the whole training set (including the noisy-labeled data),
which encourages generalization and prevents overfitting to the corrupted
labels. Experiments on both synthetically generated incorrect labels and
realistic large-scale noisy datasets demonstrate that our approach achieves
state-of-the-art performance with a high tolerance to severe noise.

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The categories of this paper are: mtrl-th
19
The categories of this paper are: eess
10
The categories of this paper are: q-bio
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
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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: adap-org
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The categories of this paper are: q-fin
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The categories of this paper are: funct-an
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The categories of this paper are: alg-geom
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The categories of this paper are: nucl-th
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-th
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The categories of this paper are: acc-phys
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The categories of this paper are: chem-ph