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Meta and Google researchers’ new data curation method could transform self-supervised learning

The new technique automatically curates balanced datasets, avoiding undersampling rare data, for training self-supervised learning models.

To solve this problem, researchers from Meta AI, Google, INRIA, and Université Paris Saclay have introduced a new technique for automatically curating high-quality datasets for self-supervised learning (SSL). Join us next week in NYC to engage with top executive leaders, delving into strategies for auditing AI models to ensure fairness, optimal performance, and ethical compliance across diverse organizations. The researchers describe the technique as a “generic curation algorithm agnostic to downstream tasks” that “allows the possibility of inferring interesting properties from completely uncurated data sources, independently of the specificities of the applications at hand.”

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