A PyTorch Implementation of DEMIPL

This is a PyTorch implementation of our paper "Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning", (NeurIPS'23).

Authors: Wei Tang, Weijia Zhang, and Min-Ling Zhang

@inproceedings{tang2023demipl,
  author    = {Wei Tang and Weijia Zhang and Min{-}Ling Zhang},
  title     = {Disambiguated Attention Embedding for Multi-Instance Partial-Label Learning},
  booktitle = {Advances in Neural Information Processing Systems 36 (NeurIPS'23), New Orleans, LA},
  year      = {2023}
}

If you are interested in multi-instance partial-label learning, the seminal work MIPLGP may be helpful to you.

@article{tang2023mipl,
  author    = {Wei Tang and Weijia Zhang and Min{-}Ling Zhang},
  title     = {Multi-Instance Partial-Label Learning: Towards Exploiting Dual Inexact Supervision},
  journal   = {Science China Information Sciences},
  year      = {2023}
}

Requirements

numpy==1.21.5
scikit_learn==1.3.1
scipy==1.7.3
torch==1.12.1+cu113

To install the requirement packages, please run the following command:

pip install -r requirements.txt

Datasets

The datasets used in this paper can be found on this link.

Demo

To reproduce the results of MNIST_MIPL dataset in the paper, please run the following command:

CUDA_VISIBLE_DEVICES=0 python main.py --ds MNIST_MIPL --ds_suffix 1 --lr 0.01 --epochs 100 --normalize false --w_entropy_A 0.001
CUDA_VISIBLE_DEVICES=0 python main.py --ds MNIST_MIPL --ds_suffix 2 --lr 0.01 --epochs 100 --normalize false --w_entropy_A 0.001
CUDA_VISIBLE_DEVICES=0 python main.py --ds MNIST_MIPL --ds_suffix 3 --lr 0.05 --epochs 100 --normalize false --w_entropy_A 0.001

This package is only free for academic usage. Have fun!