AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.
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Updated
Sep 19, 2024 - Python
AIMET is a library that provides advanced quantization and compression techniques for trained neural network models.
AIMET GitHub pages documentation
Model Compression Toolkit (MCT) is an open source project for neural network model optimization under efficient, constrained hardware. This project provides researchers, developers, and engineers advanced quantization and compression tools for deploying state-of-the-art neural networks.
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💍 Efficient tensor decomposition-based filter pruning
Deep Neural Network Compression based on Student-Teacher Network
Group Fisher Pruning for Practical Network Compression(ICML2021)
Neural Network Distiller by Intel AI Lab: a Python package for neural network compression research. https://intellabs.github.io/distiller
Pytorch implemenation of "Learning Filter Basis for Convolutional Neural Network Compression" ICCV2019
Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression. CVPR2020.
This is the official implementation of "DHP: Differentiable Meta Pruning via HyperNetworks".
[NeurIPS 2021] Official PyTorch Code of Scaling Up Exact Neural Network Compression by ReLU Stability
李宏毅教授 ML 2020 機器學習課程筆記 & 實作
Using ideas from product quantization for state-of-the-art neural network compression.
Code implementation of our AISTATS'21 paper "Mirror Descent View for Neural Network Quantization"
MUSCO: MUlti-Stage COmpression of neural networks
MUSCO: Multi-Stage COmpression of neural networks
Neural Network Quantization & Low-Bit Fixed Point Training For Hardware-Friendly Algorithm Design
Overparameterization and overfitting are common concerns when designing and training deep neural networks. Network pruning is an effective strategy used to reduce or limit the network complexity, but often suffers from time and computational intensive procedures to identify the most important connections and best performing hyperparameters. We s…
Official PyTorch implementation of "A Comprehensive Overhaul of Feature Distillation" (ICCV 2019)
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