An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

An Automatic Model Compression (AutoMC) framework for developing smaller and faster AI applications.

NLP DNN Toolkit - Building Your NLP DNN Models Like Playing Lego

Pretrained language model and its related optimization techniques developed by Huawei Noah's Ark Lab.

[CVPR2020] Surpassing MobileNetV3: "GhostNet: More Features from Cheap Operations"

Channel Pruning for Accelerating Very Deep Neural Networks (ICCV'17)

A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.

A PyTorch implementation for exploring deep and shallow knowledge distillation (KD) experiments with flexibility

model compression based on pytorch (1、quantization: 16/8/4/2 bits(dorefa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、ternary/binary value(twn/bnn/xnor-net)；2、 pruning: normal、regular and group convolutional channel pruning；3、 group convolution structure；4、batch-normalization folding for quantization)

A curated list of neural network pruning resources.

A list of high-quality (newest) AutoML works and lightweight models including 1.) Neural Architecture Search, 2.) Lightweight Structures, 3.) Model Compression, Quantization and Acceleration, 4.) Hyperparameter Optimization, 5.) Automated Feature Engineering.

Awesome Knowledge-Distillation. 分类整理的知识蒸馏paper(2014-2020)。

Lightweight and Scalable framework that combines mainstream algorithms of Click-Through-Rate prediction based computational DAG, philosophy of Parameter Server and Ring-AllReduce collective communication.

PaddleSlim is an open-source library for deep model compression and architecture search.

[CVPR2020] GhostNet: More Features from Cheap Operations

knowledge distillation papers

Data Efficient Model Compression

Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration (CVPR 2019 Oral)

Papers for deep neural network compression and acceleration

Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

[ECCV 2018] AMC: AutoML for Model Compression and Acceleration on Mobile Devices

Deep Face Model Compression

Infrastructures™ for Machine Learning Training/Inference in Production.

Java interface for fastText

PyTorch Implementation of Weights Pruning

Model Compression CLI Tool for Keras.

[ECCV 2018] AMC: AutoML for Model Compression and Acceleration on Mobile Devices

Efficient Contextualized Representation: Language Model Pruning for Sequence Labeling

Awesome machine learning model compression research papers, tools, and learning material.

⛵️The official PyTorch implementation for "BERT-of-Theseus: Compressing BERT by Progressive Module Replacing".