yoshitomo-matsubara/yoshitomo-matsubara.github.io

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yoshitomo-matsubara/yoshitomo-matsubara.github.io

yoshitomo-matsubara

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torchdistill

A coding-free framework built on PyTorch for reproducible deep learning studies. 🏆20 knowledge distillation methods presented at CVPR, ICLR, ECCV, NeurIPS, ICCV, etc are implemented so far. 🎁 Trained models, training logs and configurations are available for ensuring the reproducibiliy and benchmark.

head-network-distillation

[IEEE Access] "Head Network Distillation: Splitting Distilled Deep Neural Networks for Resource-constrained Edge Computing Systems" and [ACM MobiCom HotEdgeVideo 2019] "Distilled Split Deep Neural Networks for Edge-assisted Real-time Systems"

Jupyter Notebook

supervised-compression

[WACV 2022] "Supervised Compression for Resource-Constrained Edge Computing Systems"

hnd-ghnd-object-detectors

[ICPR 2020] "Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged Networks" and [ACM MobiCom EMDL 2020] "Split Computing for Complex Object Detectors: Challenges and Preliminary Results"

Jupyter Notebook

sc2-benchmark

[TMLR] "SC2 Benchmark: Supervised Compression for Split Computing"

uci-cs273a-project

Basic Support for Final Projects in UCI CS 273A: Machine Learning

bottlefit-split_computing

[IEEE WoWMoM 2022] "BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computing"

split-beam

[ICDCS 2023] SplitBeam: Effective and Efficient Beamforming in Wi-Fi Networks Through Split Computing

section-categorization

Automated Section Categorization in Scientific Papers

hadoop-example

Run example codes on your computer to understand how Hadoop works