This repository is the official implementation of EXoN: EXplainable encoder Network with Tensorflow 2.0.
python==3.7
numpy==1.19.5
tensorflow==2.4.0
Additional package requirements for this repository are described in requirements.txt.
.
+-- logs (folder which contains results of all experiments: tensorboard)
| +-- mnist_100 (with 100 labeled datasets)
| +-- mnist_pre_design (Appendix A.2)
| +-- cifar10_4000 (with 4000 labeled datasets)
+-- mnist (folder which contains source codes for MNIST dataset experiments)
| +-- configs (folder which contains configuration informations of MNIST dataset experiments)
| +-- mnist_100.yaml
| +-- mnist_pre_design.yaml
| +-- preprocess.py
| +-- model.py
| +-- criterion.py
| +-- mixup.py
| +-- main.py
| +-- result.py
| +-- pre_design.py (Appendix A.2)
| +-- pre_design_result.py (Appendix A.2)
(source codes for CIFAR-10 dataset experiments)
+-- configs (folder which contains configuration informations of CIFAR-10 dataset experiments)
| +-- cifar10_4000.yaml
+-- preprocess.py
+-- model.py
+-- criterion.py
+-- mixup.py
+-- main.py
+-- result.py
+-- requirements.txt
+-- accuracy.py
+-- accuracy.txt
+-- LICENSE
+-- README.md
labeled_examples is the number of labeled datsets for running and we provide configuration .yaml files for 100 labeled datsets of MNIST and 4000 labeled datasets of CIFAR-10. And we add required tests and evaluations at the end of code.
- MNIST datset experiment
python mnist/main.py --config_path "configs/mnist_{labeled_examples}.yaml"
- for Appedix A.2,
python mnist/pre_design.py --config_path "configs/mnist_{labeled_examples}.yaml"
To get visualization results of MNIST dataset experiment and Appendix A.2, use mnist/result.py and mnist/pre_design_result.py (following codes line by line).
- CIFAR-10 dataset experiment
python main.py --config_path "configs/cifar10_{labeled_examples}.yaml"
To get visualization results of CIFAR-10 dataset experiment, use result.py (following the code line by line).
To get summarized results of repeated CIFAR-10 dataset experiments, use accuracy.py (following the code line by line).
- between same classes
- between different classes
@article{an2023custom,
author = {SeungHwan An and Jong-June Jeon},
doi = {https://doi.org/10.1016/j.patrec.2023.11.018},
issn = {0167-8655},
journal = {Pattern Recognition Letters},
title = {Customization of latent space in semi-supervised variational autoencoder},
url = {https://www.sciencedirect.com/science/article/pii/S0167865523003288},
year = {2023},