  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:
https://github.com/MalongTech/CurriculumNet .
