论文标题
使用极其嘈杂的标签学习时，可靠的标签校正是一个很好的助推器
Reliable Label Correction is a Good Booster When Learning with Extremely Noisy Labels
论文作者
论文摘要
自数据注释（尤其是对于大型数据集）以来，使用嘈杂的标签学习引起了很多研究兴趣，这可能不可避免地不可避免。最近的方法通过将培训样本分为清洁和嘈杂的集合，诉诸半监督的学习问题。然而，在重标签噪声下，这种范式容易出现显着的变性，因为干净的样品的数量太小，无法进行常规方法表现良好。在本文中，我们介绍了一个新颖的框架，称为LC-Booster，以在极端噪音的情况下明确处理学习。 LC-Booster的核心思想是将标签校正纳入样品选择中，因此，通过可靠的标签校正，可以将更多纯化的样品用于培训，从而减轻确认偏差。实验表明，LC-助推器的进步在几个嘈杂标签的基准测试中提高了最先进的结果，包括CIFAR-10，CIFAR-100，CLASTINGING 1M和WEBVISION。值得注意的是，在极端的90 \％噪声比下，LC-Booster在CIFAR-10和CIFAR-100上获得了92.9 \％和48.4 \％的精度，超过了最新方法，超过了最新的方法。
Learning with noisy labels has aroused much research interest since data annotations, especially for large-scale datasets, may be inevitably imperfect. Recent approaches resort to a semi-supervised learning problem by dividing training samples into clean and noisy sets. This paradigm, however, is prone to significant degeneration under heavy label noise, as the number of clean samples is too small for conventional methods to behave well. In this paper, we introduce a novel framework, termed as LC-Booster, to explicitly tackle learning under extreme noise. The core idea of LC-Booster is to incorporate label correction into the sample selection, so that more purified samples, through the reliable label correction, can be utilized for training, thereby alleviating the confirmation bias. Experiments show that LC-Booster advances state-of-the-art results on several noisy-label benchmarks, including CIFAR-10, CIFAR-100, Clothing1M and WebVision. Remarkably, under the extreme 90\% noise ratio, LC-Booster achieves 92.9\% and 48.4\% accuracy on CIFAR-10 and CIFAR-100, surpassing state-of-the-art methods by a large margin.
