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Found 2 papers, 1 papers with code
Training on Thin Air: Improve Image Classification with Generated Data
In this paper, we present Diffusion Inversion, a simple yet effective method that leverages the pre-trained generative model, Stable Diffusion, to generate diverse, high-quality training data for image classification.
Denoising Diffusion Probabilistic Models for Robust Image Super-Resolution in the Wild
Diffusion models have shown promising results on single-image super-resolution and other image- to-image translation tasks.
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