Learning Efficient Convolutional Networks through Network Slimming, In ICCV 2017.
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Updated
May 13, 2019 - Python
Learning Efficient Convolutional Networks through Network Slimming, In ICCV 2017.
A research library for pytorch-based neural network pruning, compression, and more.
[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
Neural network weights prune in a static LoRA–like way
Code Implementation of On Model Compression for Neural Networks: Framework, Algorithm, and Convergence Guarantee
Implementation of Neuron Pruning with weight pruning
Code for "Characterising Across Stack Optimisations for Deep Convolutional Neural Networks"
Deep learning framework for skin cancer classification with VGG16, attention modules, model compression, and FPGA optimization.
Split a single neural network into multiple smaller networks using weight splitting.
Implementation of self-pruning neural network that automatically removes unnecessary neurons during training to reduce model size while maintaining accuracy. Built with PyTorch.
analysing Model Pruning and Unit Pruning on a large dense MNIST network
Pruning is <3
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