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Found 2 papers, 1 papers with code
Cross-to-merge training with class balance strategy for learning with noisy labels
Although sample selection is a mainstream method in the field of learning with noisy labels, which aims to mitigate the impact of noisy labels during model training, the testing performance of these methods exhibits significant fluctuations across different noise rates and types.
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Learning with noisy labels
on Clothing1M
Improved Mix-up with KL-Entropy for Learning From Noisy Labels
On the websites, there exist a lot of image data which contains inaccurate annotations, but training on these datasets may make networks easier to over-fit the noisy labels and cause performance degradation.
