Papers with Code 아카이브

Model Compression

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평가표: 2
보존된 평가 기록: 14

Model Compression is an actively pursued area of research over the last few years with the goal of deploying state-of-the-art deep networks in low-power and resource limited devices without significant drop in accuracy. Parameter pruning, low-rank factorization and weight quantization are some of the proposed methods to compress the size of deep networks.

Source: KD-MRI: A knowledge distillation framework for image reconstruction and image restoration in MRI workflow

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/AhrazA/modelcompression-2019

Deep Neural Network Compression for Image Classification and Object Detection

https://github.com/AlexandruBurlacu/keras_squeezenet

SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

https://github.com/Arthurizijar/Bert_Airport

Well-Read Students Learn Better: On the Importance of Pre-training Compact Models

https://github.com/Banus/caffe-demo

SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

https://github.com/Caesarzhang/bert-zh

Well-Read Students Learn Better: On the Importance of Pre-training Compact Models

https://github.com/Chonwai/Learning_BERT

Well-Read Students Learn Better: On the Importance of Pre-training Compact Models

https://github.com/DTU-PAS/awesome-dynn-for-cv

A Survey on Dynamic Neural Networks: from Computer Vision to Multi-modal Sensor Fusion

https://github.com/Daniel-H-99/Patient-Knowledge-Distillation

Patient Knowledge Distillation for BERT Model Compression

https://github.com/Dawars/SqueezeNet-tf

SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

https://github.com/DeadAt0m/LSQ-PyTorch

Learned Step Size Quantization

https://github.com/DeadAt0m/LSQFakeQuantize-PyTorch

Learned Step Size Quantization

https://github.com/DeligientSloth/QQsim

Well-Read Students Learn Better: On the Importance of Pre-training Compact Models

https://github.com/DeligientSloth/bert-tensorflow

Well-Read Students Learn Better: On the Importance of Pre-training Compact Models

https://github.com/Devwalkar/BOC-KD

Online Ensemble Model Compression using Knowledge Distillation

https://github.com/Gedeon-m-gedus/compressed_object_detection

Compressed Object Detection

https://github.com/Goandwanderfaraway/squeezenet-mindspore

SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

https://github.com/Guang000/Awesome-Dataset-Distillation/blob/main/README.md

FedSynth: Gradient Compression via Synthetic Data in Federated Learning

https://github.com/Hao840/manifold-distillation

Learning Efficient Vision Transformers via Fine-Grained Manifold Distillation

https://github.com/Holldean/BERT-Pruning

Structured Pruning of Large Language Models

https://github.com/JDAI-CV/dabnn

Forward and Backward Information Retention for Accurate Binary Neural Networks

https://github.com/Jastot/Rodinka_Neural_Network

SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

https://github.com/JiahaoYan98/Pruning-ADMM

Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM

https://github.com/Kaido0/Brain-Tissue-Segment-Keras

SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

https://github.com/KaiyangZhou/mixstyle-release

On-Device Domain Generalization

https://github.com/KarenUllrich/Tutorial_BayesianCompressionForDL

Soft Weight-Sharing for Neural Network Compression

https://github.com/Kelvinyu1117/LSQ-implementation

Learned Step Size Quantization

https://github.com/KentaItakura/Classify-crack-image-and-explain-why-using-MATLAB

SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

https://github.com/King-Otaku/Emotion_Recognition_DNN

SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

https://github.com/LTH14/FSKD

Few Sample Knowledge Distillation for Efficient Network Compression

https://github.com/Luccadoremi/Model-Compression-DAQ

Training with Quantization Noise for Extreme Model Compression

https://github.com/MOHAMEDELDAKDOUKY/bert_adjusted

Well-Read Students Learn Better: On the Importance of Pre-training Compact Models

https://github.com/MS-Mind/MS-Code-02/tree/main/configs/squeezenet

SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

https://github.com/Mayurji/Image-Classification-PyTorch

SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

https://github.com/Maz101/Bert

Well-Read Students Learn Better: On the Importance of Pre-training Compact Models

https://github.com/Maz101/pretrainingdataset

Well-Read Students Learn Better: On the Importance of Pre-training Compact Models

https://github.com/Mind23-2/MindCode-116

SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size

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