MingSun-Tse / Efficient-Deep-LearningLinks
Collection of recent methods on (deep) neural network compression and acceleration.
☆955Updated 9 months ago
Alternatives and similar repositories for Efficient-Deep-Learning
Users that are interested in Efficient-Deep-Learning are comparing it to the libraries listed below
Sorting:
- ☆670Updated 4 years ago
- Summary, Code for Deep Neural Network Quantization☆559Updated 6 months ago
- Awesome machine learning model compression research papers, quantization, tools, and learning material.☆540Updated last year
- Papers for deep neural network compression and acceleration☆403Updated 4 years ago
- Rethinking the Value of Network Pruning (Pytorch) (ICLR 2019)☆1,517Updated 5 years ago
- A curated list of neural network pruning resources.☆2,484Updated last year
- [ECCV 2018] AMC: AutoML for Model Compression and Acceleration on Mobile Devices☆447Updated 2 years ago
- knowledge distillation papers☆766Updated 2 years ago
- A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures,…☆855Updated 4 years ago
- PyTorch implementation of 'Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding' by …☆424Updated 5 years ago
- Slimmable Networks, AutoSlim, and Beyond, ICLR 2019, and ICCV 2019☆927Updated 2 years ago
- [ICLR 2020] Once for All: Train One Network and Specialize it for Efficient Deployment☆1,939Updated 2 years ago
- PyTorch library to facilitate development and standardized evaluation of neural network pruning methods.☆432Updated 2 years ago
- [ICLR 2019] ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware☆1,451Updated last year
- PyTorch Implementation of [1611.06440] Pruning Convolutional Neural Networks for Resource Efficient Inference☆886Updated 6 years ago
- Network Slimming (Pytorch) (ICCV 2017)☆918Updated 5 years ago
- Pruning Neural Networks with Taylor criterion in Pytorch