解决Webly标记数据中的分布外标签噪声
Addressing out-of-distribution label noise in webly-labelled data
摘要：
深度学习社区的一个反复关注的焦点是减少标记工作。使用搜索引擎进行数据收集和注释是生成完全人工注释和人工收集的数据集的简单替代方案。虽然网络抓取是非常有时间效率的，但一些检索到的图像是非常嘈杂的，即不正确的标记。设计健壮的算法来训练从网络上收集的噪声数据是一个重要的研究视角，这将使数据集的构建变得更容易。在本文中，我们进行了一项研究，以了解使用搜索引擎构建数据集时预期的标签噪声类型。我们回顾了目前的限制，国家的最先进的方法来处理嘈杂的标签的图像分类任务的情况下，网络噪声分布。我们提出了一个简单的解决方案，使用动态软化分布外样本（DSOS）来弥合完全干净的数据集的差距，我们在CIFAR-100数据集的损坏版本上设计了该解决方案，并与最先进的算法进行了比较网络噪声扰动MiniImageNet和斯坦福数据集以及真实标签噪声数据集：WebVision 1.0和Clothing 1 M。我们的工作是完全可复制的https://git.io/JKGcj
英文摘要：
A recurring focus of the deep learning community is towards reducing the labeling effort. Data gathering and annotation using a search engine is a simple alternative to generating a fully human-annotated and human-gathered dataset. Although web crawling is very time efficient, some of the retrieved images are unavoidably noisy, i.e. incorrectly labeled. Designing robust algorithms for training on noisy data gathered from the web is an important research perspective that would render the building of datasets easier. In this paper we conduct a study to understand the type of label noise to expect when building a dataset using a search engine. We review the current limitations of state-of-the-art methods for dealing with noisy labels for image classification tasks in the case of web noise distribution. We propose a simple solution to bridge the gap with a fully clean dataset using Dynamic Softening of Out-of-distribution Samples (DSOS), which we design on corrupted versions of the CIFAR-100 dataset, and compare against state-of-the-art algorithms on the web noise perturbated MiniImageNet and Stanford datasets and on real label noise datasets: WebVision 1.0 and Clothing1M. Our work is fully reproducible https://git.io/JKGcj
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
