Given N pre-trained deep learning models, the task is to estimate the rank of models regrading their performance on an unlabeled test set.

• python 3.6.10
• keras 2.6.0
• tensorflow 2.5.1
• scipy 1.5.4
• numpy 1.19.5

MNIST, CIFAR-10, and Fashion-MNIST are available in Keras.

Amazon and iwildcam are taken from WILDS.

Java250 and C++1000 are taken from Project CodeNet.

Download the OOD data of MNIST from Google drive or generate it by

python gene_mnist.py

Download the OOD data of CIFAR-10 from Google drive or generate it by

python gene_cifar10.py

Download the OOD data of Amazon and iwildCam from WILDS.

Download the OOD data of Java250 from Google drive.

Download all the models from Google drive.

You can also train the models for MNIST and CIFAR-10 by running the scripts in trainModel/mnist and trainModel/cifar10.

To speed the execution and avoid calling the model repeatedly, we first get the model prediction. E.g.:

python main_ground.py --dataName mnist

To get the results by baseline methods (SDS, Random, CES), run the following code:

python main_selection.py --dataName mnist --metric random

Besides, to get the final results of CES, you need to run:

python main_ces_best.py --dataName mnist

To get the results by LaF, run the following code:

python main_laf.py --dataName mnist --dataType id

To get the evaluation on kendall's tau, spearman's coefficients, jaccard similarity, run the following code:

python main_eva.py --dataName mnist 

[Notice] Be careful with the saving directories.

@article{10.1145/3611666,
author = {Hu, Qiang and Guo, Yuejun and Xie, Xiaofei and Cordy, Maxime and Papadakis, Mike and Le Traon, Yves},
title = {LaF: Labeling-Free Model Selection for Automated Deep Neural Network Reusing},
year = {2023},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
issn = {1049-331X},
url = {https://doi.org/10.1145/3611666},
doi = {10.1145/3611666},
journal = {ACM Trans. Softw. Eng. Methodol.},
month = {jul}
}