CoReDi coevolves semantic representations with the diffusion model via a jointly learned linear projection stabilized by stop-gradient, normalization, and regularization, yielding faster convergence and higher sample quality than fixed-representation baselines.
Advances in neural information processing systems33, 6840–6851 (2020) 2
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LDNLM replaces the quadratic similarity and averaging steps of nonlocal means with deep convolutional feature extraction and linear attention operations to produce a linear-complexity denoiser for multiplicative noise that retains traditional NLM interpretability.
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Coevolving Representations in Joint Image-Feature Diffusion
CoReDi coevolves semantic representations with the diffusion model via a jointly learned linear projection stabilized by stop-gradient, normalization, and regularization, yielding faster convergence and higher sample quality than fixed-representation baselines.
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