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Recent studies have shown that the denoising process in (generative) diffusion models can induce meaningful (discriminative) representations inside the model, though the quality of these representations still lags behind those learned through recent self-supervised learning methods.
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Diffusion models beat GANs on image synthesis
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Diffusion models beat GANs on image classification
Soumik Mukhopadhyay, Matthew Gwilliam, Vatsal Agarwal, Namitha Padmanabhan, Archana Swaminathan, Srinidhi Hegde, Tianyi Zhou, and Abhinav Shrivastava · 2021
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Generating images with sparse representations
Charlie Nash, Jacob Menick, Sander Dieleman, and Peter W Battaglia · 2021
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Improved denoising diffusion probabilistic models
Alexander Quinn Nichol and Prafulla Dhariwal · 2021
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Learning transferable visual models from natural language supervision
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Projected GANs converge faster
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Score-based generative modeling through stochastic differential equations
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Cross-modal contrastive learning for text-to-image generation
Han Zhang, Jing Yu Koh, Jason Baldridge, Honglak Lee, and Yinfei Yang · 2021
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Flow straight and fast: Learning to generate and transfer data with rectified flow
Xingchao Liu, Chengyue Gong, and Qiang Liu · 2023
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William Peebles and Saining Xie · 2023
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SDXL: Improving latent diffusion models for high-resolution image synthesis
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Denoising diffusion autoencoders are unified self-supervised learners
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Diffusion model as representation learner
Xingyi Yang and Xinchao Wang · 2023
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Pre-training via denoising for molecular property prediction
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Classifier-free diffusion guidance
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Elucidating the design space of diffusion-based generative models
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Sigmoid loss for language image pre-training
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Analyzing and improving the training dynamics of diffusion models
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Understanding diffusion objectives as the ELBO with simple data augmentation
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Applying guidance in a limited interval improves sample and distribution quality in diffusion models
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