Awasome Research Papers and other Resources in Convolution Neural Network Pruning. This collection is prepared inspired by he-y/Awesome-Pruning.
Notation | Explanation |
---|---|
U |
Unstructured, Weight Pruning |
S |
Structured, Filter/Channel Pruning |
A |
Official/Author Implementation |
O |
Unofficial/3rd Party Implementation |
Year | Venue | Title | Type | Code |
---|---|---|---|---|
2022 |
FasterAI | FasterAI: Prune and Distill your models with FastAI and PyTorch | U |
PyTorch[A] |
2022 |
SoftwareX | Simplify: A Python library for optimizing pruned neural networks | PyTorch[A] | |
2021 |
github.com | Toolbox: Structural Pruning for Model Acceleration | S |
PyTorch[A] |
2021 |
github.com | Toolbox: PyTorchViz [A small package to create visualizations of PyTorch execution graphs] | PyTorch[A] | |
2020 |
MLSys |
What is the State of Neural Network Pruning? | S/U |
PyTorch[A] |
2019 |
PyTorch | Official PyTorch Pruning Tool | S/U |
PyTorch[A] |