Learning with noisy labels is a vital topic for practical deep learning as
models should be robust to noisy open-world datasets in the wild. The
state-of-the-art noisy label learning approach JoCoR fails when faced with a
large ratio of noisy labels. Moreover, selecting small-loss samples can also
cause error accumulation as once the noisy samples are mistakenly selected as
small-loss samples, they are more likely to be selected again. In this paper,
we try to deal with error accumulation in noisy label learning from both model
and data perspectives. We introduce mean point ensemble to utilize a more
robust loss function and more information from unselected samples to reduce
error accumulation from the model perspective. Furthermore, as the flip images
have the same semantic meaning as the original images, we select small-loss
samples according to the loss values of flip images instead of the original
ones to reduce error accumulation from the data perspective. Extensive
experiments on CIFAR-10, CIFAR-100, and large-scale Clothing1M show that our
method outperforms state-of-the-art noisy label learning methods with different
levels of label noise. Our method can also be seamlessly combined with other
noisy label learning methods to further improve their performance and
generalize well to other tasks. The code is available in
this https URL.

本文通过模型和数据维度尝试处理嘈杂标签学习中的误差积累问题。引入均值点集成方法来利用更鲁棒的损失函数和未被选择样本中的更多信息从模型角度减少误差积累；从数据角度，选择翻转图像的损失值来代替原始图像的损失值选择小损失样本，以减少误差积累。在 CIFAR-10、CIFAR-100 和大规模 Clothing1M 上的广泛实验表明，我们的方法优于不同噪声水平的最先进的嘈杂标签学习方法，也可以与其他噪声标签学习方法无缝结合以进一步提高它们的性能并很好地推广给其他任务。