README

Official repository for Multi-Instance Embedding Learning with Deconfounded Instance-Level Prediction (MEDI).
Any question can contact with inki.yinji@gmail.com
My home pages:

• https://inkiyinji.top
• https://inkiyinji.blog.csdn.net
• https://www.kaggle.com/inkiyinji

---

How to use

Just run Main.py

The file details

• B2B.py: The distance function.
• BagLoader.py: The generator for mnist and fanshionmnist data sets.
• Classifier.py: The instance-level classifier, such as SVM and $k$NN.
• MIL.py: The prototype of multi-instance learning (MIL), which can generate improtant variables, e.g., the number of bags $N$, the number of instances $n$.
• Main.py: The main function for MEDI.
• NN.py: The optimizer for MEDI based on the attention mechanism.
• utils.py: Some basic functions.

The third party library

numpy, pandas, torch, sklearn, scipy

The Python version

3.9.2

Some parameters for experiments

• po_label: The main class for generator. For example, if po_label = 0 and data_type="mnist" for MnistLoader, the fashionmnist0 data set (data_space) will be used.
• file_name:
  • If bag_space == None: The data set under the specified path will be used.
  • If bag_space != None: This variable is just used to print the file_name, and the data in bag_sapce will be used.
• epoch: The epoches for optimizer.
• loops: The loops times the k-cv.
• Others:
  • lr: The learning rate.
  • max_dim: The dimension of embedding vector of the bag.
  • norm_type: The norm type for loss function.
  • distill_type: The distill type for embedding function.

Citation

You can cite our paper as:

@article{Zhang:2022:multi,
author      =	{Yu-Xuan Zhangand Mei Yang and Zheng Chun Zhou and Fan Min},
title       =	{Multi-instance embedding learning with deconfounded instance-level prediction},
journal     =	{Research Square},
year        =	{2022},
doi         =	{10.21203/rs.3.rs-1729204/v1},
url         =	{https://www.researchsquare.com/article/rs-1729204/v1}
}