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ososos888 avatar

ososos888/prune-then-distill

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Prune Then Distill

This is an PyTorch implement of the paper ``Prune Your Model Before Distill It''.

Features

  • Weight Pruning - Combining pruning and distillation for efficient model compression.

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50 estrellas·6 forks·Python·1 vista

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Preguntas frecuentes

¿Qué hace ososos888/prune-then-distill?

This is an PyTorch implement of the paper ``Prune Your Model Before Distill It''.

¿Cuáles son las características principales de ososos888/prune-then-distill?

Las características principales de ososos888/prune-then-distill son: Weight Pruning.

¿Qué alternativas de código abierto existen para ososos888/prune-then-distill?

Las alternativas de código abierto para ososos888/prune-then-distill incluyen: tencent/pocketflow — PocketFlow is an integrated toolkit for deep learning model compression, distributed training, and mobile format… chrundle/biprop — This method identifies a binary weight or binary weight and activation subnetwork within a randomly initialized… dchiji-ntt/iterand — by Daiki Chijiwa\*, Shin’ya Yamaguchi, Yasutoshi Ida, Kenji Umakoshi, Tomohiro Inoue. dem123456789/pruning-deep-neural-networks-from-a-sparsity-perspective — [ICLR 2023] Pruning Deep Neural Networks from a Sparsity Perspective. densoitlab/bitprune — This is the official repo for ICLR 2023 Paper "Bit-Pruning: A Sparse Multiplication-Less Dot-Product" Yusuke Sekikawa… boone891214/sanity-check-lth — Sample code use for NeurIPS 2021 paper: Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the…

Alternativas open-source a Prune Then Distill

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  • tencent/pocketflowAvatar de Tencent

    Tencent/PocketFlow

    2,914Ver en GitHub↗

    PocketFlow is an integrated toolkit for deep learning model compression, distributed training, and mobile format optimization. It provides a system for reducing the size and complexity of neural networks to improve inference efficiency, featuring a dedicated engine for knowledge distillation and a mobile model optimizer. The framework differentiates itself through an automated hyperparameter tuning system that uses reinforcement learning and statistical models to determine optimal compression ratios and layer-wise bit allocation. It also includes a distributed training system that utilizes mu

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  • chrundle/bipropAvatar de chrundle

    chrundle/biprop

    51Ver en GitHub↗

    This method identifies a binary weight or binary weight and activation subnetwork within a randomly initialized network that achieves performance comparable to, and sometimes better than, a weight-optimized network. The resulting binarized and pruned networks that achieve comparable performance…

    Python
    Ver en GitHub↗51
  • dchiji-ntt/iterandAvatar de dchiji-ntt

    dchiji-ntt/iterand

    10Ver en GitHub↗

    by Daiki Chijiwa\*, Shin’ya Yamaguchi, Yasutoshi Ida, Kenji Umakoshi, Tomohiro Inoue

    Python
    Ver en GitHub↗10
  • boone891214/sanity-check-lthAvatar de boone891214

    boone891214/sanity-check-LTH

    8Ver en GitHub↗

    Sample code use for NeurIPS 2021 paper: Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot?

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    Ver en GitHub↗8
Ver las 30 alternativas a Prune Then Distill→