ChoiceNet

TensorFlow Implementation of ChoiceNet on regression tasks.

Summarized result:

Classification / Regression

Paper: arxiv

Classification (MNIST) Result

Error type: [Permutation]

name	Result
Outlier Rate: 25.0%
Outlier Rate: 45.0%
Outlier Rate: 47.5%

Error type: [Random Shuffle]

name	Result
Outlier Rate: 50.0%
Outlier Rate: 90.0%
Outlier Rate: 95.0%

Error type: [Label Bias]

name	Result
Outlier Rate: 25.0%
Outlier Rate: 45.0%
Outlier Rate: 47.5%

Regression Result

Reference Function: [cosexp]

name	Training Data	Multi-Layer Perceptron	Mixture Density Network	ChoiceNet
oRate: 0.0%
oRate: 10.0%
oRate: 30.0%
oRate: 50.0%
oRate: 60.0%
oRate: 70.0%

Reference Function: [linear]

name	Training Data	Multi-Layer Perceptron	Mixture Density Network	ChoiceNet
oRate: 0.0%
oRate: 10.0%
oRate: 30.0%
oRate: 50.0%
oRate: 60.0%
oRate: 70.0%

Reference Function: [step]

name	Training Data	Multi-Layer Perceptron	Mixture Density Network	ChoiceNet
oRate: 0.0%
oRate: 10.0%
oRate: 30.0%
oRate: 50.0%
oRate: 60.0%
oRate: 70.0%

HowTo?

• run code/main_reg_run.ipynb
• Properly modify followings based on the working environment:

nWorker = 16
maxGPU  = 8

• (I was using 16 CPUs / 8 TESLA P40s / 96GB RAM.)

Requirements

• Python3
• TF 1.4>=

Contact

• [email protected]
• [email protected]
• [email protected]

This work was done in Kakao Brain.