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18 search results found

Model Optimization ⭐ 1,445

A toolkit to optimize ML models for deployment for Keras and TensorFlow, including quantization and pruning.

Neural Network Compression Framework for enhanced OpenVINO™ inference

Network Slimming ⭐ 499

Network Slimming (Pytorch) (ICCV 2017)

Caffe for Sparse and Low-rank Deep Neural Networks

Sparse Structured Attention ⭐ 190

Sparse and structured neural attention mechanisms

Sparse Evolutionary Artificial Neural Networks ⭐ 187

Always sparse. Never dense. But never say never. A Sparse Training repository for the Adaptive Sparse Connectivity concept and its algorithmic instantiation, i.e. Sparse Evolutionary Training, to boost Deep Learning scalability on various aspects (e.g. memory and computational time efficiency, representation and generalization power).

Deep Compression ⭐ 173

Learning both Weights and Connections for Efficient Neural Networks https://arxiv.org/abs/1506.02626

Selecsls Pytorch ⭐ 165

Reference ImageNet implementation of SelecSLS CNN architecture proposed in the SIGGRAPH 2020 paper "XNect: Real-time Multi-Person 3D Motion Capture with a Single RGB Camera". The repository also includes code for pruning the model based on implicit sparsity emerging from adaptive gradient descent methods, as detailed in the CVPR 2019 paper "On implicit filter level sparsity in Convolutional Neural Networks".

Torchprune ⭐ 130

A research library for pytorch-based neural network pruning, compression, and more.

Mmdetection ⭐ 81

OpenVINO Training Extensions Object Detection

In Time Over Parameterization ⭐ 42

[ICML 2021] "Do We Actually Need Dense Over-Parameterization? In-Time Over-Parameterization in Sparse Training" by Shiwei Liu, Lu Yin, Decebal Constantin Mocanu, Mykola Pechenizkiy

Attention Guided Sparsity ⭐ 25

Attention-Based Guided Structured Sparsity of Deep Neural Networks

Smc Bench ⭐ 21

[ICLR 2023] "Sparsity May Cry: Let Us Fail (Current) Sparse Neural Networks Together!" Shiwei Liu, Tianlong Chen, Zhenyu Zhang, Xuxi Chen, Tianjin Huang, AJAY KUMAR JAISWAL, Zhangyang Wang

Quickselection ⭐ 15

[Machine Learning Journal (ECML-PKDD 2022 journal track)] Quick and Robust Feature Selection: the Strength of Energy-efficient Sparse Training for Autoencoders

Pruning Deep Neural Networks From A Sparsity Perspective ⭐ 12

[ICLR 2023] Pruning Deep Neural Networks from a Sparsity Perspective

Ssld Face_recognition ⭐ 11

Robust Single Sample Face Recognition by Sparsity-Driven Sub-Dictionary Learning Using Deep Features

Compressed Dnns Forget ⭐ 9

Minimal Reproducibility Study of (https://arxiv.org/abs/1911.05248). Experiments with Compression of Deep Neural Networks

Sparse Dynamic Synapses ⭐ 7

"The Unreasonable Effectiveness of Sparse Dynamic Synapses for Continual Learning" paper project.

Torch_topkast ⭐ 6

PyTorch Implementation of TopKAST

Dynamic Sparse Training For Deep Reinforcement Learning ⭐ 5

[IJCAI 2022] "Dynamic Sparse Training for Deep Reinforcement Learning" by Ghada Sokar, Elena Mocanu , Decebal Constantin Mocanu, Mykola Pechenizkiy, and Peter Stone.

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