ProMix: Combating Label Noise via Maximizing Clean Sample Utility
- URL: http://arxiv.org/abs/2207.10276v4
- Date: Thu, 3 Aug 2023 12:20:15 GMT
- Title: ProMix: Combating Label Noise via Maximizing Clean Sample Utility
- Authors: Ruixuan Xiao, Yiwen Dong, Haobo Wang, Lei Feng, Runze Wu, Gang Chen,
Junbo Zhao
- Abstract summary: ProMix is a framework to maximize the utility of clean samples for boosted performance.
It achieves an average improvement of 2.48% on the CIFAR-N dataset.
- Score: 18.305972075220765
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Learning with Noisy Labels (LNL) has become an appealing topic, as
imperfectly annotated data are relatively cheaper to obtain. Recent
state-of-the-art approaches employ specific selection mechanisms to separate
clean and noisy samples and then apply Semi-Supervised Learning (SSL)
techniques for improved performance. However, the selection step mostly
provides a medium-sized and decent-enough clean subset, which overlooks a rich
set of clean samples. To fulfill this, we propose a novel LNL framework ProMix
that attempts to maximize the utility of clean samples for boosted performance.
Key to our method, we propose a matched high confidence selection technique
that selects those examples with high confidence scores and matched predictions
with given labels to dynamically expand a base clean sample set. To overcome
the potential side effect of excessive clean set selection procedure, we
further devise a novel SSL framework that is able to train balanced and
unbiased classifiers on the separated clean and noisy samples. Extensive
experiments demonstrate that ProMix significantly advances the current
state-of-the-art results on multiple benchmarks with different types and levels
of noise. It achieves an average improvement of 2.48\% on the CIFAR-N dataset.
The code is available at https://github.com/Justherozen/ProMix
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