We present an approach to effectively use millions of images with noisy
annotations in conjunction with a small subset of cleanly-annotated images to
learn powerful image representations. One common approach to combine clean and
noisy data is to first pre-train a network using the large noisy dataset and
then fine-tune with the clean dataset. We show this approach does not fully
leverage the information contained in the clean set. Thus, we demonstrate how
to use the clean annotations to reduce the noise in the large dataset before
fine-tuning the network using both the clean set and the full set with reduced
noise. The approach comprises a multi-task network that jointly learns to clean
noisy annotations and to accurately classify images. We evaluate our approach
on the recently released Open Images dataset, containing ~9 million images,
multiple annotations per image and over 6000 unique classes. For the small
clean set of annotations we use a quarter of the validation set with ~40k
images. Our results demonstrate that the proposed approach clearly outperforms
direct fine-tuning across all major categories of classes in the Open Image
dataset. Further, our approach is particularly effective for a large number of
classes with wide range of noise in annotations (20-80% false positive
annotations).

本文介绍了一种使用大型包含嘈杂注释图像和少量清洁注释图像联合进行学习的方法，它由一个多任务网络组成，可以在清洁注释的帮助下减少大型数据集中的噪音，并在清洁数据集和带有降噪的完整数据集上进行微调学习，其结果比直接微调方法在 Open Image 数据集中的所有主要类别中均表现优异，特别适用于具有 20-80％注释噪音的大量类别。