pytorch implementation of "Distilling a Neural Network Into a Soft Decision Tree"

Last update: Dec 04, 2022

Overview

Soft-Decision-Tree

Soft-Decision-Tree is the pytorch implementation of Distilling a Neural Network Into a Soft Decision Tree, paper recently published on Arxiv about adopting decision tree algorithm into neural network. "If we could take the knowledge acquired by the neural net and express the same knowledge in a model that relies on hierarchical decisions instead, explaining a particular decision would be much easier."

Requirements

python
pytorch

Result

I achieved 92.95% of test dataset accuracy on MNISTafter 40 epoches, without exploring enough of hyper-parameters (The paper achieved 94.45%). Higher accuracy might be achievable with searching hyper-parameters, or training longer epoches (if you can, please let me know :) )

Usage

$ python main.py

Owner

Kim Heecheol

University of Tokyo, Intelligent systems & Informatics Lab.

GitHub Repository https://arxiv.org/pdf/1711.09784.pdf

pytorch implementation of "Distilling a Neural Network Into a Soft Decision Tree"

Related tags

Overview

Soft-Decision-Tree

Requirements

Result

Usage

Owner

Kim Heecheol

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