Weakly Supervised Learning with Side Information for Noisy Labeled Images

Lele Cheng, Xiangzeng Zhou, Liming Zhao, Dangwei Li, Hong Shang, Yun Zheng, Pan Pan, Yinghui Xu ;

In many real-world datasets, like WebVision, the performance of DNN based classier is often limited by the noisy labeled data. To tackle this problem, some image related side information, such as captions and tags, often reveal underlying relationships across images. In this paper, we present an efficient weakly-supervised learning by using a Side Information Network (SINet), which aims to effectively carry out a large scale classication with severely noisy labels. The proposed SINet consists of a visual prototype module and a noise weighting module. The visual prototype module is designed to generate a compact representation for each category by introducing the side information. The noise weighting module aims to estimate the correctness of each noisy image and produce a condence score for image ranking during the training procedure. The propsed SINet can largely alleviate the negative impact of noisy image labels, and is benecial to train a high performance CNN based classier. Besides, we release a ne-grained product dataset called AliProducts, which contains more than 2.5 million noisy web images crawled from the internet by using queries generated from 50,000 fine-grained semantic classes. Extensive experiments on several popular benchmarks (i.e. Webvision, ImageNet and Clothing-1M) and our proposed AliProducts achieve state-of-the-art performance. The SINet has won the rst place in the 5000 category classication task on WebVision Challenge 2019, and outperforms other competitors by a large margin."