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                   Readme for the BayesDGC & BayesGC Package
 		       version July, 2020

The package includes the code of BayesDGC & BayesGC, which solves the crowdsourcing aggregation problem with Bayesian generative learning respectively using features by dnn and only using annotations[1].

[1] S.-Y. Li, S.-J. Huang and S. Chen. Crowdsourcing Aggregation with Deep Bayesian Learning, submitted to Science China Information Science.

ATTN:

  • This package is free for academic usage. You can run it at your own risk. For other purposes, please contact Ms. Shao-Yuan Li(lisy@nuaa.edu.cn).

  • This package was developed by Ms. Shao-Yuan Li (lisy@nuaa.edu.cn). For any problem concerning the code, please feel free to contact Ms. Li.

Code description: BayesDGC.py: the BayesDGC model proposed in [1] which used both features and annotations for Bayesian deep crowd aggreation. BayesGC.py: the BayesGC model proposed in [1] which used only annotations for Bayesian crowd aggreation. ThreshPostProcess/ThreshPostProcess.m: the post processing MATLAB code of getting the top-K prediction results where K is the number of postive examples.

data_sample/: one dataset sample used in [1], corresponds to the dataset1 l=1

To get results of the two models, run BayesDGC.py in vs code, then run ThreshPostProcess.m in matlab