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chainization's Introduction

Chainization

Official pytorch implementation of ECCV 2022 paper, "Order Learning Using Partially Ordered Data via Chainization."

Dependencies

  • Python 3.8
  • Pytorch 1.7.1

Datasets

  • For MORPH II experiments, we follow the same fold settings in this OL repo.
  • For Adience experiments, we follow the official splits.

Quick Start : Train Model on Random Edge Cases

You can adjust supervision ratio by changing 'info_ratio' in the parse_option function.

  • for Adience dataset

    $ python train_chainize_adience.py 
  • for MORPH II dataset

    $ python train_chainize_morph.py

Referecences

  1. FixMatch
  2. POE

chainization's People

Contributors

seon92 avatar

Stargazers

 avatar DY avatar Manxi Lin avatar  avatar Jaeseung Lee avatar Dongkwon Jin avatar

Watchers

 avatar

chainization's Issues

Contact

Dear Seon-Ho Lee,
I am reaching out to you here because I was not able to find a different method.

I would like to inquire about your paper Moving Window Regression: A Novel Approach to Ordinal Regression.

Specifically, I want to ask about the regressor architecture. As I describe in the Issue nhshin-mcl/MWR#13 on the official repository, the paper mentions using fully connected layers in the regressor. However, in the code, the model seems to be implemented using convolutions.

Could you elaborate on this, please?
You can also contact me at [email protected]

Thank you.

Best wishes,
Jakub

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