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Controlled Training Data Generation with Diffusion Models
In this work, we present a method to control a text-to-image generative model to produce training data specifically "useful" for supervised learning.
Unraveling the Key Components of OOD Generalization via Diversification
We show that (1) diversification methods are highly sensitive to the distribution of the unlabeled data used for diversification and can underperform significantly when away from a method-specific sweet spot.
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