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We introduce an approach to bias deep generative models, such as GANs and diffusion models, towards generating data with either enhanced fidelity or increased diversity.
Imagenet: A large-scale hierarchical image database
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Diffusion models beat gans on image synthesis
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Taming transformers for high-resolution image synthesis
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Alias-free generative adversarial networks
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Improved denoising diffusion probabilistic models
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Projected gans converge faster
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Variational feature disentangling for fine-grained few-shot classification
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Assessing sample quality via the latent space of generative models
Jingyi Xu, Hieu Le, and Dimitris Samaras · 2024
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