Learning Efficient Convolutional Networks through Network Slimming, In ICCV 2017.

A research library for pytorch-based neural network pruning, compression, and more.

[ICML 2023] UPop: Unified and Progressive Pruning for Compressing Vision-Language Transformers.

[ICCV2023 Official PyTorch code] for Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution

Knowledge distillation from Ensembles of Iterative pruning (BMVC 2020)

Image captioning with weight pruning in PyTorch

(Unstructured) Weight Pruning via Adaptive Sparsity Loss

Feather is a module that enables effective sparsification of neural networks during training. This repository accompanies the paper "Feather: An Elegant Solution to Effective DNN Sparsification" (BMVC2023).

TensorFlow implementation of weight and unit pruning and sparsification

Pruning

Neural network weights prune in a static LoRA–like way

Implementation of Neuron Pruning with weight pruning

Code Implementation of On Model Compression for Neural Networks: Framework, Algorithm, and Convergence Guarantee

Code for "Characterising Across Stack Optimisations for Deep Convolutional Neural Networks"

analysing Model Pruning and Unit Pruning on a large dense MNIST network

Pruning is <3