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Quality-Diversity Generative Sampling for Learning with Synthetic Data

By Allen Chang and others at
LogoMIT
and
LogoUniversity of Southern California
Generative models can serve as surrogates for some real data sources by creating synthetic training datasets, but in doing so they may transfer biases to downstream tasks. We focus on protecting quality and diversity when generating synthetic training datasets. We propose quality-diversity generative sampling (QDGS), a framework for sampling data... Show more
February 27, 2024
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Quality-Diversity Generative Sampling for Learning with Synthetic Data
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