  This paper focuses on webly supervised learning (WSL), where datasets are
built by crawling samples from the Internet and directly using search queries
as web labels. Although WSL benefits from fast and low-cost data collection,
noises in web labels hinder better performance of the image classification
model. To alleviate this problem, in recent works, self-label supervised loss
$\mathcal{L}_s$ is utilized together with webly supervised loss
$\mathcal{L}_w$. $\mathcal{L}_s$ relies on pseudo labels predicted by the model
itself. Since the correctness of the web label or pseudo label is usually on a
case-by-case basis for each web sample, it is desirable to adjust the balance
between $\mathcal{L}_s$ and $\mathcal{L}_w$ on sample level. Inspired by the
ability of Deep Neural Networks (DNNs) in confidence prediction, we introduce
Self-Contained Confidence (SCC) by adapting model uncertainty for WSL setting,
and use it to sample-wisely balance $\mathcal{L}_s$ and $\mathcal{L}_w$.
Therefore, a simple yet effective WSL framework is proposed. A series of
SCC-friendly regularization approaches are investigated, among which the
proposed graph-enhanced mixup is the most effective method to provide
high-quality confidence to enhance our framework. The proposed WSL framework
has achieved the state-of-the-art results on two large-scale WSL datasets,
WebVision-1000 and Food101-N. Code is available at
https://github.com/bigvideoresearch/SCC.
