EigenDamage-Pytorch
Pruning algorithm
A deep learning project implementing structured pruning algorithms in PyTorch for efficient neural network training and inference.
Code for "EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis" https://arxiv.org/abs/1905.05934
112 stars
5 watching
19 forks
Language: Python
last commit: over 6 years agodeep-learningfisher-matrixmachine-learningnetwork-pruningpytorch
Related projects:
| Repository | Description | Stars |
|---|---|---|
| This project provides a PyTorch implementation of pruning techniques to reduce the computational resources required for neural network inference. | 877 | |
| A Python framework for building deep learning models with optimized encoding layers and batch normalization. | 2,044 | |
| A tool for training neural networks with pruned weights and evaluating their performance. | 140 | |
| An implementation of a deep learning model using PyTorch for semantic segmentation tasks. | 237 | |
| A PyTorch toolbox for supporting research and development of domain adaptation, generalization, and semi-supervised learning methods in computer vision. | 1,236 | |
| A comprehensive tutorial on deep learning for natural language processing with PyTorch, covering the basics and advancing to linguistic structure prediction. | 1,942 | |
| Implementation of a deep learning model for generating high-quality images with improved stability and variation. | 538 | |
| An implementation of an optimization algorithm for training neural networks in machine learning environments. | 351 | |
| An implementation of synthetic gradients to decouple neural network layers and enable scalable communication between them | 119 | |
| A PyTorch framework simplifying neural network training with automated boilerplate code and callback utilities | 572 | |
| A lightweight wrapper around PyTorch to prevent CUDA out-of-memory errors and optimize model execution | 1,823 | |
| An open source implementation of deep learning-based human segmentation models using PyTorch | 558 | |
| An implementation of an A3C algorithm for reinforcement learning in Pytorch, with various optimizations and extensions to accelerate training. | 562 | |
| An implementation of Advantage async Actor-Critic Algorithms in PyTorch for Deep Reinforcement Learning | 114 | |
| A PyTorch implementation of an optimization algorithm for continuous control and reinforcement learning tasks | 435 |