A Knowledge Distillation-Based Approach to Enhance Transparency of Classifier Models
Authors: Yuchen Jiang, Xinyuan Zhao, Yihang Wu, Ahmad Chaddad
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We selected three public medical data sets (brain tumor, eye disease, and Alzheimer s disease) to test our method. It shows that even when the number of layers is reduced, our model provides a remarkable result in the test set and reduces the time required for the interpretability analysis. |
| Researcher Affiliation | Academia | Artificial Intelligence for Personalised Medicine, School of Artificial Intelligence, Guilin University of Electronic Technology Jinji Road, Guilin 541004, China EMAIL |
| Pseudocode | Yes | Algorithm 1 provides a basic procedure of our approach. |
| Open Source Code | Yes | Code https://github.com/AIPMLab/KD-FMV |
| Open Datasets | Yes | We selected three public medical data sets (brain tumor, eye disease, and Alzheimer s disease) to test our method. Brain tumor: This dataset (Nickparvar 2021) is a collection that combines three separate data sets: figshare (Figshare 2017-04-03), the SARTAJ (Bhuvaji 2020), and Br35H (Hamada 2020). Eyes disease: This dataset consists of 4217 Colour fundus photography (CFP)... (Doddi 2023). Alzheimer: The dataset consists of MRI images... (Kaggle 2022). |
| Dataset Splits | Yes | Brain tumor: ...our training-to-validation ratio is 8:2. Eyes disease: We randomly divided the images according to training, validation and testing with a ratio of 7:2:1. Alzheimer: The images are randomly divided into training, validation, and testing ratios of 7:2:1. For detailed information on the training/validation and testing sets, refer to Table 1. |
| Hardware Specification | Yes | We used an Intel i9-13900k, an NVIDIA Ge Force RTX 4090, and Tensor Flow-gpu 2.6.0 for our simulations. |
| Software Dependencies | Yes | We used an Intel i9-13900k, an NVIDIA Ge Force RTX 4090, and Tensor Flow-gpu 2.6.0 for our simulations. |
| Experiment Setup | Yes | We used consistent hyperparameter settings, employed the Adam optimizer, set the learning rate to 1 10 4, and configured the batch size to 16. |