Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SELC: Self-Ensemble Label Correction Improves Learning with Noisy Labels   Deep neural networks are prone to overfitting noisy labels, resulting in poor
generalization performance. To overcome this problem, we present a simple and
effective method self-ensemble label correction (SELC) to progressively correct
noisy labels and refine the model. We look deeper into the memorization
behavior in training with noisy labels and observe that the network outputs are
reliable in the early stage. To retain this reliable knowledge, SELC uses
ensemble predictions formed by an exponential moving average of network outputs
to update the original noisy labels. We show that training with SELC refines
the model by gradually reducing supervision from noisy labels and increasing
supervision from ensemble predictions. Despite its simplicity, compared with
many state-of-the-art methods, SELC obtains more promising and stable results
in the presence of class-conditional, instance-dependent, and real-world label
noise. The code is available at https://github.com/MacLLL/SELC.

34
The categories of this paper are: chem-ph
17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
2
The categories of this paper are: math
16
The categories of this paper are: stat
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
11
The categories of this paper are: hep-lat
22
The categories of this paper are: chao-dyn
32
The categories of this paper are: atom-ph
27
The categories of this paper are: comp-gas