Keras model convolutional filter pruning package
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Updated
Feb 16, 2019 - Python
Keras model convolutional filter pruning package
Knowledge distillation from Ensembles of Iterative pruning (BMVC 2020)
Constraint-Aware Importance Estimation for Global Filter Pruning under Multiple Resource Constraints (CVPRW2020)
Cheng-Hao Tu, Jia-Hong Lee, Yi-Ming Chan and Chu-Song Chen, "Pruning Depthwise Separable Convolutions for MobileNet Compression," International Joint Conference on Neural Networks, IJCNN 2020, July 2020.
Official Pytorch implementation of "Filter Pruning by Image Channel Reduction in Pre-Trained Convolutional Neural Networks".
[ICLR'21] Neural Pruning via Growing Regularization (PyTorch)
Official repository for the research article "Pruning vs XNOR-Net: A ComprehensiveStudy on Deep Learning for AudioClassification in Microcontrollers"
[NIPS 2016] Learning Structured Sparsity in Deep Neural Networks
Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression. CVPR2020.
An easy way to conduct filter-pruning for Convolutional layers and fully connected layers
[NeurIPS'21 Spotlight] Aligned Structured Sparsity Learning for Efficient Image Super-Resolution (PyTorch)
[Master Thesis] Research project at the Data Analytics Lab in collaboration with Daedalean AI. The thesis was submitted to both ETH Zürich and Imperial College London.
code for your paper "Discrete cosine transform for filter pruning"
Code for CHIP: CHannel Independence-based Pruning for Compact Neural Networks (NeruIPS 2021).
Ensemble Knowledge Guided Sub-network Search and Fine-tuning for Filter Pruning
A research library for pytorch-based neural network pruning, compression, and more.
GNN-RL Compression: Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning
对人像抠图模型MODNet进行滤波器级别的剪枝,结合自适应与固定比例策略。
In the human synaptic system, there are two important channels known as excitatory and inhibitory neurotransmitters that transmit a signal from a neuron to a cell. Adopting the neuroscientific perspective, we propose a synapse-inspired filter pruning method.
Filter pruning techniques of convolutional neural networks implemented with the Darknet framework.
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