Well Begun Is Half Done: Generating High-Quality Seeds for Automatic Image Dataset Construction from Web

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Learning Visual Features from Large Weakly Supervised Data (2015, neutral): “In contrast to our work, many prior studies also attempt to explicitly discard low-quality labels by developing algorithms that identify relevant image-hashtag pairs from a weakly labeled dataset [12, 35, 54].”
Webly Supervised Learning of Convolutional Networks (2015, methodology): “One can use speciﬁcally designed techniques [9, 12] and loss layers [52, 42] to alleviate some of these problems.”
Generating Diverse Image Datasets with Limited Labeling (2016, background): “To address the issues stated above and inspired by streams of images available on the web, there has been lot of recent interest in learning directly from noisy web data [20, 28, 18, 4, 14, 15, 5] or automatically curating web images for creating a dataset [33, 23, 31, 6, 1].”
Automatic Construction of Action Datasets Using Web Videos with Density-Based Cluster Analysis and Outlier Detection (2015, methodology): “As distance metric, we use Rank-order distance [26] which has been demonstrated as a better density measurement than commonly used Euclidean distance [17].”
Learning Discriminative Reconstructions for Unsupervised Outlier Removal (2015, background): “Retrieving images from search engines [22, 5, 14, 25] is one of the most effective solutions.”