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A recurring focus of the deep learning community is towards reducing thelabeling effort. Data gathering and annotation using a search engine is asimple alternative to generating a fully human-annotated and human-gathereddataset. Although web crawling is very time efficient, some of the retrievedimages are unavoidably noisy, i.e. incorrectly labeled. Designing robustalgorithms for training on noisy data gathered from the web is an importantresearch perspective that would render the building of datasets easier. In thispaper we conduct a study to understand the type of label noise to expect whenbuilding a dataset using a search engine. We review the current limitations ofstate-of-the-art methods for dealing with noisy labels for image classificationtasks in the case of web noise distribution. We propose a simple solution tobridge the gap with a fully clean dataset using Dynamic Softening ofOut-of-distribution Samples (DSOS), which we design on corrupted versions ofthe CIFAR-100 dataset, and compare against state-of-the-art algorithms on theweb noise perturbated MiniImageNet and Stanford datasets and on real labelnoise datasets: WebVision 1.0 and Clothing1M. Our work is fully reproduciblehttps://git.io/JKGcj

Addressing out-of-distribution label noise in webly-labelled data