[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
OTOv1-v3, NeurIPS, ICLR, TMLR, DNN Training, Compression, Structured Pruning, Erasing Operators, CNN, Diffusion, LLM
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
Knowledge distillation from Ensembles of Iterative pruning (BMVC 2020)
💍 Efficient tensor decomposition-based filter pruning
Towards Meta-Pruning via Optimal Transport, ICLR 2024 (Spotlight)
2SSP: A Two-Stage Framework for Structured Pruning of LLMs
Code Implementation for "NASH: A Simple Unified Framework of Structured Pruning for Accelerating Encoder-Decoder Language Models" (EMNLP 2023)
Structured pruning and bias visualization for Large Language Models. Tools for LLM optimization and fairness analysis.
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.
🌠 Enhanced Network Compression Through Tensor Decompositions and Pruning
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.
[Project] Structured/Unstructured Pruning Comparison Experiment
[Project] Edge computing Intra-Fusion Comparison Experiment
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