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Model Compression

39 papers with code · Methodology

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Greatest papers with code

The State of Sparsity in Deep Neural Networks

25 Feb 2019google-research/google-research

We rigorously evaluate three state-of-the-art techniques for inducing sparsity in deep neural networks on two large-scale learning tasks: Transformer trained on WMT 2014 English-to-German, and ResNet-50 trained on ImageNet.

MODEL COMPRESSION

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

24 Feb 2016pytorch/vision

(2) Smaller DNNs require less bandwidth to export a new model from the cloud to an autonomous car.

MODEL COMPRESSION

Model compression via distillation and quantization

ICLR 2018 NervanaSystems/distiller

Deep neural networks (DNNs) continue to make significant advances, solving tasks from image classification to translation or reinforcement learning.

MODEL COMPRESSION QUANTIZATION

AMC: AutoML for Model Compression and Acceleration on Mobile Devices

ECCV 2018 NervanaSystems/distiller

Model compression is a critical technique to efficiently deploy neural network models on mobile devices which have limited computation resources and tight power budgets.

MODEL COMPRESSION NEURAL ARCHITECTURE SEARCH

Contrastive Representation Distillation

23 Oct 2019HobbitLong/RepDistiller

We demonstrate that this objective ignores important structural knowledge of the teacher network.

MODEL COMPRESSION TRANSFER LEARNING

Global Sparse Momentum SGD for Pruning Very Deep Neural Networks

NeurIPS 2019 ShawnDing1994/ACNet

Deep Neural Network (DNN) is powerful but computationally expensive and memory intensive, thus impeding its practical usage on resource-constrained front-end devices.

MODEL COMPRESSION

Discrimination-aware Channel Pruning for Deep Neural Networks

NeurIPS 2018 SCUT-AILab/DCP

Channel pruning is one of the predominant approaches for deep model compression.

MODEL COMPRESSION

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

19 Nov 2017cuguilke/microexpnet

This paper is aimed at creating extremely small and fast convolutional neural networks (CNN) for the problem of facial expression recognition (FER) from frontal face images.

FACIAL EXPRESSION RECOGNITION MODEL COMPRESSION

A Programmable Approach to Model Compression

6 Nov 2019NVlabs/condensa

However, while the results are desirable, finding the best compression strategy for a given neural network, target platform, and optimization objective often requires extensive experimentation.

IMAGE CLASSIFICATION LANGUAGE MODELLING MODEL COMPRESSION QUANTIZATION

Focused Quantization for Sparse CNNs

NeurIPS 2019 deep-fry/mayo

In ResNet-50, we achieved a 18. 08x CR with only 0. 24% loss in top-5 accuracy, outperforming existing compression methods.

NEURAL NETWORK COMPRESSION QUANTIZATION