Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Confidence Scores Make Instance-dependent Label-noise Learning Possible   In learning with noisy labels, for every instance, its label can randomly
walk to other classes following a transition distribution which is named a
noise model. Well-studied noise models are all instance-independent, namely,
the transition depends only on the original label but not the instance itself,
and thus they are less practical in the wild. Fortunately, methods based on
instance-dependent noise have been studied, but most of them have to rely on
strong assumptions on the noise models. To alleviate this issue, we introduce
confidence-scored instance-dependent noise (CSIDN), where each instance-label
pair is equipped with a confidence score. We find with the help of confidence
scores, the transition distribution of each instance can be approximately
estimated. Similarly to the powerful forward correction for
instance-independent noise, we propose a novel instance-level forward
correction for CSIDN. We demonstrate the utility and effectiveness of our
method through multiple experiments under synthetic label noise and real-world
unknown noise.

13
The categories of this paper are: quant-ph
10
The categories of this paper are: q-bio
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
5
The categories of this paper are: gr-qc
2
The categories of this paper are: math
18
The categories of this paper are: econ
0
The categories of this paper are: hep-ph
26
The categories of this paper are: bayes-an
14
The categories of this paper are: math-ph
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The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
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The categories of this paper are: adap-org
3
The categories of this paper are: physics
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The categories of this paper are: alg-geom
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The categories of this paper are: acc-phys