Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Generalization by Recognizing Confusion   A recently-proposed technique called self-adaptive training augments modern
neural networks by allowing them to adjust training labels on the fly, to avoid
overfitting to samples that may be mislabeled or otherwise non-representative.
By combining the self-adaptive objective with mixup, we further improve the
accuracy of self-adaptive models for image recognition; the resulting
classifier obtains state-of-the-art accuracies on datasets corrupted with label
noise. Robustness to label noise implies a lower generalization gap; thus, our
approach also leads to improved generalizability. We find evidence that the
Rademacher complexity of these algorithms is low, suggesting a new path towards
provable generalization for this type of deep learning model. Last, we
highlight a novel connection between difficulties accounting for rare classes
and robustness under noise, as rare classes are in a sense indistinguishable
from label noise. Our code can be found at
https://github.com/Tuxianeer/generalizationconfusion.

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The categories of this paper are: physics
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
28
The categories of this paper are: alg-geom
17
The categories of this paper are: q-fin
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
10
The categories of this paper are: q-bio
18
The categories of this paper are: econ
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The categories of this paper are: math
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
27
The categories of this paper are: comp-gas
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The categories of this paper are: cmp-lg
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The categories of this paper are: plasm-ph
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The categories of this paper are: hep-ex