2021
DOI: 10.48550/arxiv.2107.07041
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Mitigating Memorization in Sample Selection for Learning with Noisy Labels

Kyeongbo Kong,
Junggi Lee,
Youngchul Kwak
et al.

Abstract: Because deep learning is vulnerable to noisy labels, sample selection techniques, which train networks with only clean labeled data, have attracted a great attention. However, if the labels are dominantly corrupted by few classes, these noisy samples are called dominant-noisy-labeled samples, the network also learns dominant-noisy-labeled samples rapidly via content-aware optimization. In this study, we propose a compelling criteria to penalize dominant-noisy-labeled samples intensively through class-wise pena… Show more

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