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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

This work was done in Kakao Brain.