AdvancedDropout

Advanced Dropout: A Model-free Methodology for Bayesian Dropout Optimization (IEEE TPAMI 2021) IEEE Xplore or ArXiv

Code List

• main.py
  • Main file for running
• mlp.py
  • Fully connected (FC) layers with advanced dropout
• variationalBayesDropout.py
  • Advanced dropout

Dataset

CIFAR-10 (and others)

Requirements

• python >= 3.6
• PyTorch >= 1.1.0
• torchvision >= 0.3.0
• GPU memory >= 3500MiB (GTX 1080Ti)

Training

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

Citation

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

@ARTICLE{9439951,
  author={Xie, Jiyang and Ma, Zhanyu and Lei, Jianjun and Zhang, Guoqiang and Xue, Jing-Hao and Tan, Zheng-Hua and Guo, Jun},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Advanced Dropout: A Model-free Methodology for Bayesian Dropout Optimization}, 
  year={2021},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/TPAMI.2021.3083089}}