Implementation for ICLR2019 paper MAX-MIG: AN INFORMATION THEORETIC APPROACH FOR JOINT LEARNING FROM CROWDS
paper link: https://openreview.net/forum?id=BJg9DoR9t7
arxiv : https://arxiv.org/abs/1905.13436
[Slide]
- To run experiments of Dogs vs. Cats dataset in
Dogs vs. Cats
directory:
python3 main.py --case case_num --expertise ex --path path_to_dataset --device device_num
case_num: number of experimental case( see our paper)
1: Independent mistakes
2: Naive majority
3: Correlated mistakes
expertise: the expertise of senior expertise
0: Low expertise
1: High expertise
path_to_dataset: path to the dataset
device_num : GPU number
- To run experiments of CIFAR-10 dataset in
Cifar10
directory:
python3 main.py --case case_num --expertise ex --path path_to_dataset --device device_num
case_num: number of experimental case( see our paper)
1: Independent mistakes
2: Naive majority
3: Correlated mistakes
expertise: the expertise of senior expertise
0: Low expertise
1: High expertise
path_to_dataset: path to the dataset
device_num : GPU number
- To run experiments of LUNA dataset in
LUNA16
directory:
python3 main.py --case case_num --expertise ex --path path_to_dataset --device device_num
case_num: number of experimental case( see our paper)
1: Independent mistakes
2: Naive majority
3: Correlated mistakes
expertise: the expertise of senior expertise
0: Low expertise
1: High expertise
path_to_dataset: path to the dataset
device_num : GPU number
- To run experiments of Labelme dataset in
labelme
directory:
python3 cotraining_labelme.py --device device_num
device_num : GPU number
The Labelme dataset can be downloaded at http://fprodrigues.com//deep_LabelMe.tar.gz . Please place prepared
document in the same folder with your code. 😄
To cite our paper:
@article{cao2018max,
title={Max-MIG: an Information Theoretic Approach for Joint Learning from Crowds},
author={Cao, Peng and Xu, Yilun and Kong, Yuqing and Wang, Yizhou},
year={2018}
}