GSNR constructs a null-restricted graph Laplacian and projects onto its smoothest modes to regularize only the null-space part of inverse problem solutions, yielding up to 4.3 dB PSNR gains when plugged into PnP, DIP, and diffusion solvers.
U- net: Convolutional networks for biomedical image segmen- tation
2 Pith papers cite this work. Polarity classification is still indexing.
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cs.CV 2representative citing papers
Reward Forcing combines EMA-Sink tokens and Rewarded Distribution Matching Distillation to deliver state-of-the-art streaming video generation at 23.1 FPS without copying initial frames.
citing papers explorer
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GSNR: Graph Smooth Null-Space Representation for Inverse Problems
GSNR constructs a null-restricted graph Laplacian and projects onto its smoothest modes to regularize only the null-space part of inverse problem solutions, yielding up to 4.3 dB PSNR gains when plugged into PnP, DIP, and diffusion solvers.
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Reward Forcing: Efficient Streaming Video Generation with Rewarded Distribution Matching Distillation
Reward Forcing combines EMA-Sink tokens and Rewarded Distribution Matching Distillation to deliver state-of-the-art streaming video generation at 23.1 FPS without copying initial frames.