Fast Multi-Instance Partial-Label Learning
Authors: Yin-Fang Yang, Wei Tang, Min-Ling Zhang
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that the performance of FASTMIPL is highly competitive to state-of-the-art methods, while significantly reducing computational time in benchmark and the real-world datasets. |
| Researcher Affiliation | Academia | Yin-Fang Yang1,2, Wei Tang1,2*, Min-Ling Zhang1,2 1School of Computer Science and Engineering, Southeast University, Nanjing 210096, China 2Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | The FASTMIPL pseudocode of the optimization procedure summarizes in the Appendix |
| Open Source Code | Yes | FASTMIPL s code and appendix have been made publicly available on Github: https://github.com/yangyf22/Fast MIPL |
| Open Datasets | Yes | Table 1 provides an overview of the characteristics of all datasets. There are eight types of characteristics mentioned. The symbol #bag denotes the count of multi-instance bags... Datasets: MNIST-MIPL (MNIST), FMNIST-MIPL (FMNIST), Birdsong-MIPL (Birdsong), SIVAL-MIPL (SIVAL), CRC-MIPL-Row (C-Row), CRC-MIPL-SBN (C-SBN), CRC-MIPL-KMeans Seg (C-KMeans), CRC-MIPL-SIFT (C-SIFT) |
| Dataset Splits | Yes | The data partition follows the strategies of DEMIPL and ELIMIPL, dividing the data into training and testing sets with a ratio of 7:3. The average and the standard deviation of accuracy are recorded by conducting the experiments with random train/test splits ten times |
| Hardware Specification | Yes | FASTMIPL is implemented using PyTorch and trained on a single NVIDIA GeForce RTX 4090 GPU. All experiments are performed on a machine with an Intel Core i7-13700K CPU, 64 GB main memory, and a single NVIDIA GeForce RTX 4090 GPU. |
| Software Dependencies | No | FASTMIPL is implemented using PyTorch and trained on a single NVIDIA Ge Force RTX 4090 GPU. |
| Experiment Setup | Yes | The optimization process employs stochastic gradient descent (SGD) with a momentum of 0.9 and a weight decay of 0.0001... The learning rate is selected from the predefined set {0.0005, 0.001, 0.002, 0.005}, the training batch size equals to the count of bags in the training set, and the value of posterior samples to approximate the expectation is chosen from the set {10, 20, 30, 40, 50}. The number of epochs is set to 200 for the MNIST-MIPL and FMNIST-MIPL datasets and 500 for the remaining three datasets. |