Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Distilling Effective Supervision from Severe Label Noise   Collecting large-scale data with clean labels for supervised training of
neural networks is practically challenging. Although noisy labels are usually
cheap to acquire, existing methods suffer a lot from label noise. This paper
targets at the challenge of robust training at high label noise regimes. The
key insight to achieve this goal is to wisely leverage a small trusted set to
estimate exemplar weights and pseudo labels for noisy data in order to reuse
them for supervised training. We present a holistic framework to train deep
neural networks in a way that is highly invulnerable to label noise. Our method
sets the new state of the art on various types of label noise and achieves
excellent performance on large-scale datasets with real-world label noise. For
instance, on CIFAR100 with a $40\%$ uniform noise ratio and only 10 trusted
labeled data per class, our method achieves $80.2{\pm}0.3\%$ classification
accuracy, where the error rate is only $1.4\%$ higher than a neural network
trained without label noise. Moreover, increasing the noise ratio to $80\%$,
our method still maintains a high accuracy of $75.5{\pm}0.2\%$, compared to the
previous best accuracy $48.2\%$.
  Source code available:
https://github.com/google-research/google-research/tree/master/ieg

32
The categories of this paper are: atom-ph
19
The categories of this paper are: eess
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
26
The categories of this paper are: bayes-an
24
The categories of this paper are: dg-ga
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
21
The categories of this paper are: adap-org
23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
20
The categories of this paper are: acc-phys
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph