Summary of Select: a Large-scale Benchmark Of Data Curation Strategies For Image Classification, by Benjamin Feuer et al.
SELECT: A Large-Scale Benchmark of Data Curation Strategies for Image Classification
by Benjamin Feuer, Jiawei Xu, Niv Cohen, Patrick Yubeaton, Govind Mittal, Chinmay Hegde
First submitted to arxiv on: 7 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a comprehensive evaluation of data curation methods for efficient learning, particularly in the context of image classification. The authors introduce SELECT, a large-scale benchmark that compares various curation strategies to determine which ones are most effective. The study aims to bridge the gap in existing research on data curation by providing a systematic comparison of different approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Data curation is important for machine learning models because it helps collect and organize samples into a dataset that supports efficient learning. Right now, there’s not much research on comparing different data curation methods. This paper tries to fix that by creating a big benchmark called SELECT that tests various ways of curating images for classification tasks. |
Keywords
» Artificial intelligence » Classification » Image classification » Machine learning
