[CVPR 2023] DepGraph: Towards Any Structural Pruning

[AAAI 2024] Fluctuation-based Adaptive Structured Pruning for Large Language Models

[NeurIPS 2023] Structural Pruning for Diffusion Models

Knowledge distillation from Ensembles of Iterative pruning (BMVC 2020)

Awasome Papers and Resources in Deep Neural Network Pruning with Source Code.

Code for CHIP: CHannel Independence-based Pruning for Compact Neural Networks (NeruIPS 2021).

OTOv1-v3, NeurIPS, ICLR, TMLR, DNN Training, Compression, Structured Pruning, Erasing Operators, CNN, Diffusion, LLM

This repository is the official implementation of the paper Pruning via Iterative Ranking of Sensitivity Statistics and implements novel pruning / compression algorithms for deep learning / neural networks. Amongst others it implements structured pruning before training, its actual parameter shrinking and unstructured before/during training.

We have implemented a framework that supports developers to structured prune neural networks of Tensorflow Models

💍 Efficient tensor decomposition-based filter pruning

Code repository for paper "Efficient Structured Pruning and Architecture Searching for Group Convolution" https://arxiv.org/abs/1811.09341

The framework to prune LLMs to any size and any config.

Deepak Ghimire, Kilho Lee, and Seong-heum Kim, Loss-aware automatic selection of structured pruning criteria for deep neural network acceleration, Image and Vision Computing, vol. 136, p. 104745, 2023.

Make Structured Pruning Methods Smooth and Adaptive: Decay Pruning Method (DPM) is a novel smooth and dynamic pruning approach, that can be seemingly integrated with various existing structured pruning methods, providing significant improvement.

About Code for the paper "NASH: A Simple Unified Framework of Structured Pruning for Accelerating Encoder-Decoder Language Models" (EMNLP 2023 Findings)