LAMP adds a lagged temporal correction derived from second-order discretization to diffusion posterior samplers, yielding consistent gains over DiffPIR and DDRM on imaging tasks via a bias-variance trade-off.
Denoising diffusion implicit models
3 Pith papers cite this work. Polarity classification is still indexing.
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PGID restores watermark detection in diffusion models by using progressive inversion-denoising cycles to correct latents displaced by removal or forgery attacks.
A confidence-guided diffusion framework generates synthetic Bangla compound characters that, when filtered and added to training data, raise classifier accuracy to 89.2% on the AIBangla dataset.
citing papers explorer
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Improving Diffusion Posterior Samplers with Lagged Temporal Corrections for Image Restoration
LAMP adds a lagged temporal correction derived from second-order discretization to diffusion posterior samplers, yielding consistent gains over DiffPIR and DDRM on imaging tasks via a bias-variance trade-off.
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PGID: Progressive Guided Inversion and Denoising for Robust Watermark Detection
PGID restores watermark detection in diffusion models by using progressive inversion-denoising cycles to correct latents displaced by removal or forgery attacks.
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Confidence-Guided Diffusion Augmentation for Enhanced Bangla Compound Character Recognition
A confidence-guided diffusion framework generates synthetic Bangla compound characters that, when filtered and added to training data, raise classifier accuracy to 89.2% on the AIBangla dataset.