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Neural Network Compression

19 papers with code · Methodology
Subtask of Model Compression

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Improving Neural Network Quantization without Retraining using Outlier Channel Splitting

28 Jan 2019NervanaSystems/distiller

The majority of existing literature focuses on training quantized DNNs, while this work examines the less-studied topic of quantizing a floating-point model without (re)training.

LANGUAGE MODELLING NEURAL NETWORK COMPRESSION QUANTIZATION

IR-Net: Forward and Backward Information Retention for Highly Accurate Binary Neural Networks

24 Sep 2019JDAI-CV/dabnn

Weight and activation binarization is an effective approach to deep neural network compression and can accelerate the inference by leveraging bitwise operations.

NEURAL NETWORK COMPRESSION QUANTIZATION

Data-Free Learning of Student Networks

ICCV 2019 huawei-noah/DAFL

Learning portable neural networks is very essential for computer vision for the purpose that pre-trained heavy deep models can be well applied on edge devices such as mobile phones and micro sensors.

NEURAL NETWORK COMPRESSION

DAFL: Data-Free Learning of Student Networks

2 Apr 2019huawei-noah/DAFL

Learning portable neural networks is very essential for computer vision for the purpose that pre-trained heavy deep models can be well applied on edge devices such as mobile phones and micro sensors.

NEURAL NETWORK COMPRESSION

Learning Sparse Networks Using Targeted Dropout

31 May 2019for-ai/TD

Before computing the gradients for each weight update, targeted dropout stochastically selects a set of units or weights to be dropped using a simple self-reinforcing sparsity criterion and then computes the gradients for the remaining weights.

NETWORK PRUNING NEURAL NETWORK COMPRESSION

Soft Weight-Sharing for Neural Network Compression

13 Feb 2017KarenUllrich/Tutorial_BayesianCompressionForDL

The success of deep learning in numerous application domains created the de- sire to run and train them on mobile devices.

NEURAL NETWORK COMPRESSION QUANTIZATION

A Closer Look at Structured Pruning for Neural Network Compression

10 Oct 2018BayesWatch/pytorch-prunes

Structured pruning is a popular method for compressing a neural network: given a large trained network, one alternates between removing channel connections and fine-tuning; reducing the overall width of the network.

NETWORK PRUNING NEURAL NETWORK COMPRESSION

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

COP: Customized Deep Model Compression via Regularized Correlation-Based Filter-Level Pruning

25 Jun 2019ZJULearning/COP

2) Cross-layer filter comparison is unachievable since the importance is defined locally within each layer.

NEURAL NETWORK COMPRESSION

MUSCO: Multi-Stage Compression of neural networks

24 Mar 2019musco-ai/musco-pytorch

The low-rank tensor approximation is very promising for the compression of deep neural networks.

NEURAL NETWORK COMPRESSION