We present a simple yet efficient approach capable of training deep neural
networks on large-scale weakly-supervised web images, which are crawled raw
from the Internet by using text queries, without any human annotation. We
develop a principled learning strategy by leveraging curriculum learning, with
the goal of handling a massive amount of noisy labels and data imbalance
effectively. We design a new learning curriculum by measuring the complexity of
data using its distribution density in a feature space, and rank the complexity
in an unsupervised manner. This allows for an efficient implementation of
curriculum learning on large-scale web images, resulting in a high-performance
CNN model, where the negative impact of noisy labels is reduced substantially.
Importantly, we show by experiments that those images with highly noisy labels
can surprisingly improve the generalization capability of the model, by serving
as a manner of regularization. Our approaches obtain state-of-the-art
performance on four benchmarks: WebVision, ImageNet, Clothing-1M and Food-101.
With an ensemble of multiple models, we achieved a top-5 error rate of 5.2% on
the WebVision challenge for 1000-category classification. This result was the
top performance by a wide margin, outperforming second place by a nearly 50%
relative error rate. Code and models are available at:
this https URL .

该论文提出了一种基于课程学习的弱监督学习方法，通过对大规模网络图像数据进行无人工标注的深度神经网络训练，实现对大量噪声数据和数据分布不均等问题的有效处理和噪声标签的负面影响的显著降低，构建一种新的课程学习架构，使得使用高噪声标签的图像作为一种正则化策略可以惊人地提高模型的泛化能力，在 WebVision、ImageNet、Clothing-1M 和 Food-101 等四个基准测试中取得了最先进的性能，多模型集成的结果在 1000 种类别分类中取得了 5.2% 的 top-5 错误率，相对误差率超过 50%。