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Found 2 papers, 2 papers with code
Minimize Quantization Output Error with Bias Compensation
Quantization is a promising method that reduces memory usage and computational intensity of Deep Neural Networks (DNNs), but it often leads to significant output error that hinder model deployment.
Resfusion: Denoising Diffusion Probabilistic Models for Image Restoration Based on Prior Residual Noise
We propose Resfusion, a general framework that incorporates the residual term into the diffusion forward process, starting the reverse process directly from the noisy degraded images.
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