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Neural Network Compression

11 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

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

Deep Neural Network Compression with Single and Multiple Level Quantization

6 Mar 2018yuhuixu1993/SLQ

In this paper, we propose two novel network quantization approaches, single-level network quantization (SLQ) for high-bit quantization and multi-level network quantization (MLQ) for extremely low-bit quantization (ternary). We are the first to consider the network quantization from both width and depth level.

NEURAL NETWORK COMPRESSION QUANTIZATION

MUSCO: Multi-Stage Compression of neural networks

24 Mar 2019juliagusak/musco

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

NEURAL NETWORK COMPRESSION

Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters

ICLR 2019 cambridge-mlg/miracle

While deep neural networks are a highly successful model class, their large memory footprint puts considerable strain on energy consumption, communication bandwidth, and storage requirements.

NEURAL NETWORK COMPRESSION QUANTIZATION

Efficient Neural Network Compression

CVPR 2019 Hyeji-Kim/ENC

The better accuracy and complexity compromise, as well as the extremely fast speed of our method makes it suitable for neural network compression.

ACCURACY METRICS NEURAL NETWORK COMPRESSION

Differentiable Fine-grained Quantization for Deep Neural Network Compression

20 Oct 2018newwhitecheng/compress-all-nn

Thus judiciously selecting different precision for different layers/structures can potentially produce more efficient models compared to traditional quantization methods by striking a better balance between accuracy and compression rate.

NEURAL NETWORK COMPRESSION QUANTIZATION

Characterising Across-Stack Optimisations for Deep Convolutional Neural Networks

19 Sep 2018jack-willturner/characterising-neural-compression

Convolutional Neural Networks (CNNs) are extremely computationally demanding, presenting a large barrier to their deployment on resource-constrained devices.

NEURAL NETWORK COMPRESSION