Model Lineage Closeness Analysis
Authors: Chen Tang, Lan Zhang, Qi Zhao, Xirong Zhuang, Xiang-Yang Li
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, comprehensive experiments show that our design achieves an impressive 97% accuracy in lineage determination and can precisely measure model lineage closeness for different modifications. |
| Researcher Affiliation | Academia | 1University of Science and Technology of China, China 2Institute of Artificial Intelligence, Hefei Comprehensive National Science Center, China EMAIL, EMAIL |
| Pseudocode | No | The paper describes methods using mathematical formulations and a workflow diagram (Fig. 2) but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | We make our code and this benchmark open-source.1 https://github.com/chentangUSTCCS/Model Lineage Closeness |
| Open Datasets | Yes | Specifically, we used four datasets: MNIST (Le Cun et al. 1998), CIFAR-10 (Krizhevsky, Hinton et al. 2009), Flower102 (Nilsback and Zisserman 2008) and SDog120 (Khosla et al. 2011), four model structures: Le Net (Le Cun et al. 1998), VGG16 (Simonyan and Zisserman 2015b), Mobile Netv2 (Sandler et al. 2018) and Res Net18 (He et al. 2016), to train two sets of models. |
| Dataset Splits | No | The paper discusses generating samples for lineage determination and a sampling method for a test set, but it does not provide specific training/test/validation splits for the datasets (e.g., MNIST, CIFAR-10) used to train the base models. |
| Hardware Specification | Yes | Moreover, all experiments are conducted on a Linux Server with 1 Tesla P100 GPU and implemented with Py Torch 1.5 using Python 3.7. |
| Software Dependencies | Yes | Moreover, all experiments are conducted on a Linux Server with 1 Tesla P100 GPU and implemented with Py Torch 1.5 using Python 3.7. |
| Experiment Setup | Yes | To determine the threshold, we generate 4 lineage models and 2 no lineage models for each source model and calculate the lineage closeness score. Then we set the threshold δ to 0.35 which can well distinguish generated lineage models and no lineage models. |