Neural Network Compression
74 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Neural Network Compression
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Libraries
Use these libraries to find Neural Network Compression models and implementations
CIFAR-10
Most implemented papers
Soft Weight-Sharing for Neural Network Compression
The success of deep learning in numerous application domains created the de- sire to run and train them on mobile devices.
Improving Neural Network Quantization without Retraining using Outlier Channel Splitting
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.
MUSCO: Multi-Stage Compression of neural networks
juliagusak/musco
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The low-rank tensor approximation is very promising for the compression of deep neural networks.
Data-Free Learning of Student Networks
huawei-noah/Data-Efficient-Model-Compression
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ICCV 2019
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.
Learning Filter Basis for Convolutional Neural Network Compression
ofsoundof/learning_filter_basis
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ICCV 2019
Convolutional neural networks (CNNs) based solutions have achieved state-of-the-art performances for many computer vision tasks, including classification and super-resolution of images.
ZeroQ: A Novel Zero Shot Quantization Framework
amirgholami/ZeroQ
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CVPR 2020
Importantly, ZeroQ has a very low computational overhead, and it can finish the entire quantization process in less than 30s (0. 5\% of one epoch training time of ResNet50 on ImageNet).
NeRV: Neural Representations for Videos
haochen-rye/nerv
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NeurIPS 2021
In contrast, with NeRV, we can use any neural network compression method as a proxy for video compression, and achieve comparable performance to traditional frame-based video compression approaches (H. 264, HEVC \etc).
Diversity Networks: Neural Network Compression Using Determinantal Point Processes
nobug-code/Diversity_Networks_CNN_Pytorch
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We introduce Divnet, a flexible technique for learning networks with diverse neurons.
Weightless: Lossy Weight Encoding For Deep Neural Network Compression
This results in up to a 1. 51x improvement over the state-of-the-art.
Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters
cambridge-mlg/miracle
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ICLR 2019
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.
