60 Open source projects that are alternatives of or similar to Collaborative Distillation

[NeurIPS'2019] Shupeng Gui, Haotao Wang, Haichuan Yang, Chen Yu, Zhangyang Wang, Ji Liu, “Model Compression with Adversarial Robustness: A Unified Optimization Framework”

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

Pytorch implementation of various Knowledge Distillation (KD) methods.

Yolov5 distillation training | Yolov5知识蒸馏训练，支持训练自己的数据

Model Compression CLI Tool for Keras.

This project is the official implementation of our accepted ICLR 2021 paper BiPointNet: Binary Neural Network for Point Clouds.

BitPack is a practical tool to efficiently save ultra-low precision/mixed-precision quantized models.

The Easiest Knowledge Distillation Library for Lightweight Deep Learning

Java interface for fastText

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

Deep Learning for Computer Vision (CommE 5052) in NTU

Efficient Contextualized Representation: Language Model Pruning for Sequence Labeling

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

[ICLR'21] PyTorch code for our paper "Neural Pruning via Growing Regularization"

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

pytorch implementation of Structured Bayesian Pruning

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

Automatic DNN compression tool with various model compression and neural architecture search techniques

An Open Source Deep Learning Inference Engine Based on FPGA

⛵️The official PyTorch implementation for "BERT-of-Theseus: Compressing BERT by Progressive Module Replacing" (EMNLP 2020).

knowledge distillation papers

A Pytorch Knowledge Distillation library for benchmarking and extending works in the domains of Knowledge Distillation, Pruning, and Quantization.

Awesome Knowledge-Distillation. 分类整理的知识蒸馏paper(2014-2021)。

PyTorch Implementation of Weights Pruning

Papers for deep neural network compression and acceleration

It is a blueprint to data science from the mathematics to algorithms. It is not completed.

yolov3 by pytorch

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

Efficient-Computing

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

🤖 A machine learning framework for audio, text, image, video, or .CSV files (50+ featurizers and 15+ model trainers).

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

Learnable Embedding Space for Efficient Neural Architecture Compression

papers about model compression

pytorch realtime multi person keypoint estimation

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.

针对pytorch模型的自动化模型结构分析和修改工具集，包含自动分析模型结构的模型压缩算法库

micronet, a model compression and deploy lib. compression: 1、quantization: quantization-aware-training(QAT), High-Bit(>2b)(DoReFa/Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference)、Low-Bit(≤2b)/Ternary and Binary(TWN/BNN/XNOR-Net); post-training-quantization(PTQ), 8-bit(tensorrt); 2、 pruning: normal、regular and group convolutional channel pruning; 3、 group convolution structure; 4、batch-normalization fuse for quantization. deploy: tensorrt, fp32/fp16/int8(ptq-calibration)、op-adapt(upsample)、dynamic_shape

CVPR 2021 : Zero-shot Adversarial Quantization (ZAQ)

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.

(CVPR 2021, Oral) Dynamic Slimmable Network

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

Infrastructures™ for Machine Learning Training/Inference in Production.

[CVPR2020] GhostNet: More Features from Cheap Operations

A pytorch pruning toolkit for structured neural network pruning and layer dependency maintaining.

Model Compression Based on Geoffery Hinton's Logit Regression Method in Keras applied to MNIST 16x compression over 0.95 percent accuracy.An Implementation of "Distilling the Knowledge in a Neural Network - Geoffery Hinton et. al"

Deep Face Model Compression

Data Efficient Model Compression

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

Quantization library for PyTorch. Support low-precision and mixed-precision quantization, with hardware implementation through TVM.

Code for "Co-Evolutionary Compression for Unpaired Image Translation" (ICCV 2019) and "SCOP: Scientific Control for Reliable Neural Network Pruning" (NeurIPS 2020).

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

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

A curated list of neural network pruning resources.

Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks

Programmable Neural Network Compression

MicroExpNet: An Extremely Small and Fast Model For Expression Recognition From Frontal Face Images

CV backbones including GhostNet, TinyNet and TNT, developed by Huawei Noah's Ark Lab.

Compressing Representations for Self-Supervised Learning

[NeurIPS'21] "Chasing Sparsity in Vision Transformers: An End-to-End Exploration" by Tianlong Chen, Yu Cheng, Zhe Gan, Lu Yuan, Lei Zhang, Zhangyang Wang