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[Preprint] Why is the State of Neural Network Pruning so Confusing? On the Fairness, Comparison Setup, and Trainability in Network Pruning

Efficient-Deep-Learning

Collection of recent methods on (deep) neural network compression and acceleration.

925

Collaborative-Distillation

[CVPR'20] Collaborative Distillation for Ultra-Resolution Universal Style Transfer (PyTorch)

Python

180

Regularization-Pruning

[ICLR'21] Neural Pruning via Growing Regularization (PyTorch)

Python

ASSL

[NeurIPS'21 Spotlight] Aligned Structured Sparsity Learning for Efficient Image Super-Resolution (PyTorch)

Python

Awesome-Pruning-at-Initialization

[IJCAI'22 Survey] Recent Advances on Neural Network Pruning at Initialization.

Smile-Pruning

A generic code base for neural network pruning, especially for pruning at initialization.

Python

Good-DA-in-KD

[NeurIPS'22] What Makes a "Good" Data Augmentation in Knowledge Distillation -- A Statistical Perspective

Python

smilelogging

Python logging package for easy reproducible experimenting in research

Python

TPP

[ICLR'23] Trainability Preserving Neural Pruning (PyTorch)

Python

Awesome-Efficient-ViT

Recent Advances on Efficient Vision Transformers

SRP

[ICLR'22] PyTorch code for our paper "Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning"

Python

Caffe_IncReg

[IJCNN'19, IEEE JSTSP'19] Caffe code for our paper "Structured Pruning for Efficient ConvNets via Incremental Regularization"; [BMVC'18] "Structured Probabilistic Pruning for Convolutional Neural Network Acceleration"

Makefile

WritingTips

"Good scientific writing is not a matter of life and death; it is much more serious than that."

TeX

Efficient-NeRF

Python

UtilsHub

Jupyter Notebook

LowlevelVision

paper collection for low-level vision

AdversarialAttacks

MingSun-Tse/Why-the-State-of-Pruning-so-Confusing

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