[CVPR 2023] DepGraph: Towards Any Structural Pruning

[NeurIPS 2023] Structural Pruning for Diffusion Models

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

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

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

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

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

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

Knowledge distillation from Ensembles of Iterative pruning (BMVC 2020)

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

💍 Efficient tensor decomposition-based filter pruning

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.

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

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.