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ELIMIPL
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# A PyTorch Implementation of ELIMIPL This is a PyTorch implementation of our paper "Exploiting Conjugate Label Information for Multi-Instance Partial-Label Learning", (**IJCAI'24**). Authors: [Wei Tang](https://tangw-seu.github.io/), [Weijia Zhang](https://www.weijiazhangxh.com/), [Min-Ling Zhang](http://palm.seu.edu.cn/zhangml/) ```bib @inproceedings{tang2024elimipl, author = {Wei Tang and Weijia Zhang and Min-Ling Zhang}, title = {Exploiting Conjugate Label Information for Multi-Instance Partial-Label Learning}, booktitle = {Proceedings of the 33rd International Joint Conference on Artificial Intelligence, Jeju, South Korea}, pages = {1--11}, year = {2024}, } ``` If you are interested in multi-instance partial-label learning, the seminal work [MIPLGP](https://tangw-seu.github.io/publications/SCIS'23.pdf) and [DEMIPL](https://tangw-seu.github.io/publications/NeurIPS'23.pdf) may be helpful to you. ```bib @article{tang2023miplgp, title = {Multi-Instance Partial-Label Learning: {T}owards Exploiting Dual Inexact Supervision}, author = {Wei Tang and Weijia Zhang and Min-Ling Zhang}, journal = {Science China Information Sciences}, volume = {67}, number = {3}, pages = {Article 132103}, year = {2024}, } @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, New Orleans, LA, USA}, pages = {56756--56771}, year = {2023}, } ``` ## Requirements ```sh numpy==1.21.5 scikit_learn==1.3.0 scipy==1.7.3 torch==1.12.0 ``` To install the requirement packages, please run the following command: ```sh pip install -r requirements.txt ``` ## Demo To reproduce the results of `MNIST-MIPL` dataset in the paper, please run the following command: ```sh CUDA_VISIBLE_DEVICES=0 python main.py --ds MNIST_MIPL --ds_suffix 1 --normalize false --lr 0.05 --epochs 100 --gamma 0.1 --mu 1. --L 128 ``` ## Parameter Settings | Dataset | learning rate | gamma | mu | | ------------------- | ------------- | ----- | ---- | | MNIST_MIPL (r=1) | 0.05 | 0.1 | 1 | | MNIST_MIPL (r=2) | 0.05 | 0.1 | 1 | | MNIST_MIPL (r=3) | 0.05 | 0.1 | 0.1 | | FMNIST_MIPL (r=1) | 0.01 | 0.5 | 1 | | FMNIST_MIPL (r=2) | 0.01 | 0.5 | 1 | | FMNIST_MIPL (r=3) | 0.05 | 0.5 | 1 | | Birdsong_MIPL (r=1) | 0.01 | 10 | 10 | | Birdsong_MIPL (r=2) | 0.01 | 10 | 10 | | Birdsong_MIPL (r=3) | 0.01 | 10 | 10 | | SIVAL_MIPL (r=1) | 0.01 | 10 | 10 | | SIVAL_MIPL (r=2) | 0.01 | 10 | 10 | | SIVAL_MIPL (r=3) | 0.05 | 10 | 10 | | CRC-MIPL-Row | 0.01 | 10 | 10 | | CRC-MIPL-SBN | 0.01 | 10 | 10 | | CRC-MIPL-KMeansSeg | 0.01 | 10 | 10 | | CRC-MIPL-SIFT | 0.01 | 10 | 10 | This package is only free for academic usage. Have fun!
