PatrickZH/Awesome-Coreset-Selection
Awesome coreset/core-set/subset/sample selection works.
This project helps researchers and practitioners in machine learning efficiently train models, especially deep learning models, by selecting a small, representative subset of their data. It provides access to academic papers and libraries that explain how to take a large dataset and identify a 'coreset' — a much smaller set of examples that still captures the essence of the original data. The primary users are machine learning engineers, data scientists, and researchers focused on model training efficiency, active learning, or continual learning.
181 stars. No commits in the last 6 months.
Use this if you need to train machine learning models faster, with less computational resources, or on very large datasets without sacrificing model performance.
Not ideal if you are looking for a plug-and-play software tool for immediate use, as this is primarily a curated collection of research and libraries requiring integration into existing ML workflows.
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Jun 30, 2024
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