  Manually annotating datasets for training deep models is very labor-intensive
and time-consuming. To overcome such inferiority, directly leveraging web
images to conduct training data becomes a natural choice. Nevertheless, the
presence of label noise in web data usually degrades the model performance.
Existing methods for combating label noise are typically designed and tested on
synthetic noisy datasets. However, they tend to fail to achieve satisfying
results on real-world noisy datasets. To this end, we propose a method named
GRIP to alleviate the noisy label problem for both synthetic and real-world
datasets. Specifically, GRIP utilizes a group regularization strategy that
estimates class soft labels to improve noise robustness. Soft label supervision
reduces overfitting on noisy labels and learns inter-class similarities to
benefit classification. Furthermore, an instance purification operation
globally identifies noisy labels by measuring the difference between each
training sample and its class soft label. Through operations at both group and
instance levels, our approach integrates the advantages of noise-robust and
noise-cleaning methods and remarkably alleviates the performance degradation
caused by noisy labels. Comprehensive experimental results on synthetic and
real-world datasets demonstrate the superiority of GRIP over the existing
state-of-the-art methods.
