DS-UI

DS-UI: Dual-Supervised Mixture of Gaussian Mixture Models for Uncertainty Inference in Image Recognition (IEEE TIP 2021) IEEE Xplore or ArXiv

Code List

• main.py
  • Main file for running
• model_resnet.py
  • Implementation for ResNet
• gmm_layer.py
  • Implementation for MoGMM-FC layer
• uncertainty_measurements.py
  • Implementation for uncertainty measurements

Backbone

ResNet

Dataset

CIFAR-10

Requirements

• python >= 3.6
• PyTorch >= 1.1.0
• torchvision >= 0.3.0
• sklearn >= 0.19.1
• GPU memory >= 5000MiB (GTX 1080Ti)

Training

• Download datasets
• Train and evaluate: python main.py or use nohup nohup python main.py >1.out 2>&1 &

Args in main.py

• savepath: Save path of checkpoint and results
• repeattimes: Times of independent repeated tests
• card: Index of the used GPU
• n_component: Number of components of each GMM in MoGMM

Citation

If you find this paper useful in your research, please consider citing:

@ARTICLE{9605222,
  author={Xie, Jiyang and Ma, Zhanyu and Xue, Jing-Hao and Zhang, Guoqiang and Sun, Jian and Zheng, Yinhe and Guo, Jun},
  journal={IEEE Transactions on Image Processing}, 
  title={{DS-UI}: {D}ual-Supervised Mixture of {G}aussian Mixture Models for Uncertainty Inference in Image Recognition}, 
  year={2021},
  volume={30},
  number={},
  pages={9208-9219},
  doi={10.1109/TIP.2021.3123555}}