8 Python Sharpness Libraries

Cascaded Deep Video Deblurring Using Temporal Sharpness Prior and Non-local Spatial-Temporal Similarity

This repository is the official PyTorch implementation of Cascaded Deep Video Deblurring Using Temporal Sharpness Prior and Non-local Spatial-Temporal Similarity

4 Dec 11, 2022

This is the official PyTorch implementation for "Sharpness-aware Quantization for Deep Neural Networks".

Sharpness-aware Quantization for Deep Neural Networks This is the official repository for our paper: Sharpness-aware Quantization for Deep Neural Netw

9 Nov 25, 2021

PyTorch implementation for "Sharpness-aware Quantization for Deep Neural Networks".

Sharpness-aware Quantization for Deep Neural Networks Recent Update 2021.11.23: We release the source code of SAQ. Setup the environments Clone the re

30 Dec 19, 2022

Efficient Sharpness-aware Minimization for Improved Training of Neural Networks

Efficient Sharpness-aware Minimization for Improved Training of Neural Networks Code for “Efficient Sharpness-aware Minimization for Improved Training

32 Oct 18, 2022

Code for the Weighted, Accelerated and Restarted Primal-dual algorithm. This algorithm achieves stable linear convergence for reconstruction from undersampled noisy measurements under an approximate sharpness condition. See the paper for details.

WARPd Code for the Weighted, Accelerated and Restarted Primal-dual algorithm. This algorithm achieves stable linear convergence for reconstruction fro

1 Apr 8, 2022

Python Sharpness Resources

Python sharpness Libraries

Cascaded Deep Video Deblurring Using Temporal Sharpness Prior and Non-local Spatial-Temporal Similarity

This is the official PyTorch implementation for "Sharpness-aware Quantization for Deep Neural Networks".

PyTorch implementation for "Sharpness-aware Quantization for Deep Neural Networks".

Efficient Sharpness-aware Minimization for Improved Training of Neural Networks

Code for the Weighted, Accelerated and Restarted Primal-dual algorithm. This algorithm achieves stable linear convergence for reconstruction from undersampled noisy measurements under an approximate sharpness condition. See the paper for details.

A python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization

Sharpness-Aware Minimization for Efficiently Improving Generalization

A PyTorch implementation of Sharpness-Aware Minimization for Efficiently Improving Generalization

Python Sharpness Resources

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Python sharpness Libraries

Cascaded Deep Video Deblurring Using Temporal Sharpness Prior and Non-local Spatial-Temporal Similarity

This is the official PyTorch implementation for "Sharpness-aware Quantization for Deep Neural Networks".

PyTorch implementation for "Sharpness-aware Quantization for Deep Neural Networks".

Efficient Sharpness-aware Minimization for Improved Training of Neural Networks

Code for the Weighted, Accelerated and Restarted Primal-dual algorithm. This algorithm achieves stable linear convergence for reconstruction from undersampled noisy measurements under an approximate sharpness condition. See the paper for details.

A python toolbox for predictive uncertainty quantification, calibration, metrics, and visualization

Sharpness-Aware Minimization for Efficiently Improving Generalization

A PyTorch implementation of Sharpness-Aware Minimization for Efficiently Improving Generalization