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SELECT: A Large-Scale Benchmark of Data Curation Strategies for Image Classification

arxiv.org · 8,993 words · saved by 1 readers

This is experimental HTML to improve accessibility. We invite you to report rendering errors. Use Alt+Y to toggle on accessible reporting links and Alt+Shift+Y to toggle off. Learn more about this project and help improve conversions. HTML conversions sometimes display errors due to content that did not convert correctly from the source. This paper uses the following packages that are not yet supported by the HTML conversion tool. Feedback on these issues are not necessary; they are known and are being worked on. Authors: achieve the best HTML results from your LaTeX submissions by following these best practices. Data curation is the problem of how to collect and organize samples into a dataset that supports efficient learning. Despite the centrality of the task, little work has been devoted towards a large-scale, systematic comparison of various curation methods. In this work, we take steps towards a formal evaluation of data curation strategies and introduce Select , the first large

Benjamin Feuer ††thanks: First two authors contributed equally. Correspondence to: bf996@nyu.edu. Jiawei Xu Niv Cohen Affiliation: NYU Patrick Yubeaton Affiliation: NYU Govind Mittal Affiliation: NYU Chinmay Hegde Affiliation: NYU Abstract Data curation is the problem of how to collect and organize samples into a dataset that supports efficient learning. Despite the centrality of the task, little work has been devoted towards a large-scale, systematic comparison of various curation methods. In this work, we take steps towards a formal evaluation of data curation strategies and…

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