Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Label Noise Modeling and Loss Correction   Despite being robust to small amounts of label noise, convolutional neural
networks trained with stochastic gradient methods have been shown to easily fit
random labels. When there are a mixture of correct and mislabelled targets,
networks tend to fit the former before the latter. This suggests using a
suitable two-component mixture model as an unsupervised generative model of
sample loss values during training to allow online estimation of the
probability that a sample is mislabelled. Specifically, we propose a beta
mixture to estimate this probability and correct the loss by relying on the
network prediction (the so-called bootstrapping loss). We further adapt mixup
augmentation to drive our approach a step further. Experiments on CIFAR-10/100
and TinyImageNet demonstrate a robustness to label noise that substantially
outperforms recent state-of-the-art. Source code is available at
https://git.io/fjsvE

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The categories of this paper are: cmp-lg
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The categories of this paper are: cond-mat
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The categories of this paper are: mtrl-th
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The categories of this paper are: bayes-an
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The categories of this paper are: nucl-th
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The categories of this paper are: nlin
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The categories of this paper are: supr-con
0
The categories of this paper are: hep-ph
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The categories of this paper are: funct-an
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The categories of this paper are: stat
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The categories of this paper are: q-alg
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The categories of this paper are: math-ph
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The categories of this paper are: astro-ph
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The categories of this paper are: q-fin
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The categories of this paper are: eess
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The categories of this paper are: ao-sci