  Many advances of deep learning techniques originate from the efforts of
addressing the image classification task on large-scale datasets. However, the
construction of such clean datasets is costly and time-consuming since the
Internet is overwhelmed by noisy images with inadequate and inaccurate tags. In
this paper, we propose a Ubiquitous Reweighting Network (URNet) that learns an
image classification model from large-scale noisy data. By observing the web
data, we find that there are five key challenges, i.e., imbalanced class sizes,
high intra-classes diversity and inter-class similarity, imprecise instances,
insufficient representative instances, and ambiguous class labels. To alleviate
these challenges, we assume that every training instance has the potential to
contribute positively by alleviating the data bias and noise via reweighting
the influence of each instance according to different class sizes, large
instance clusters, its confidence, small instance bags and the labels. In this
manner, the influence of bias and noise in the web data can be gradually
alleviated, leading to the steadily improving performance of URNet.
Experimental results in the WebVision 2018 challenge with 16 million noisy
training images from 5000 classes show that our approach outperforms
state-of-the-art models and ranks the first place in the image classification
task.
