The success of learning with noisy labels (LNL) methods relies heavily on the success of a warm-up stage where standard supervised training is performed using the full (noisy) training set. In this paper, we identify a "warm-up obstacle": the inability of standard warm-up stages to train high quality feature extractors and avert memorization of noisy labels. We propose "Contrast to Divide" (C2D), a simple framework that solves this problem by pre-training the feature extractor in a self-supervised fashion. Using self-supervised pre-training boosts the performance of existing LNL approaches by drastically reducing the warm-up stage's susceptibility to noise level, shortening its duration, and increasing extracted feature quality. C2D works out of the box with existing methods and demonstrates markedly improved performance, especially in the high noise regime, where we get a boost of more than 27% for CIFAR-100 with 90% noise over the previous state of the art. In real-life noise settings, C2D trained on mini-WebVision outperforms previous works both in WebVision and ImageNet validation sets by 3% top-1 accuracy. We perform an in-depth analysis of the framework, including investigating the performance of different pre-training approaches and estimating the effective upper bound of the LNL performance with semi-supervised learning. Code for reproducing our experiments is available at https://github.com/ContrastToDivide/C2D

翻译：使用吵闹标签(LNL)方法的成功学习在很大程度上取决于一个热热阶段的成功,在这个阶段,使用完整的( noisy)培训来进行标准监管的培训。在本文中,我们发现一个“温热障碍 ” : 标准暖热阶段无法培训高质量的地物提取器,避免对吵闹标签进行记忆化。我们建议“C2D ” (C2D),这是一个简单的框架,通过以自我监督的方式对地物提取器进行预培训来解决这个问题。在现实生活中,利用自我监督的训练前训练来提高现有的LNL方法的性能。 大幅降低暖化阶段对噪音程度的敏感度,缩短其持续时间,提高提取的特性质量。 C2D用现有方法在盒子之外工作,展示出显著改进的性能,特别是在高噪音制度下,我们为CFAR-C-100提供了超过27%的提升率,比以往的近90%的噪音。 在现实生活中,C2D 培训微型WevisionionD 超越了现有的LL方法。 在高级图像-L- 的精确度分析中,我们用高级图像- 的校正前的校前的校前的校正 。