Utilized Generative Adversarial Network (GAN) to interpret the black-box deep image classifier models by PyTorch.
Methodology described in the paper by Zhao, Yunxia, 2020
- The project was implemented and tested in Python 3.5 and Pytorch 0.4. The higher versions should work after minor modification.
- Other common modules like numpy, pandas and seaborn for visualization.
- NVIDIA GPU and cuDNN are required to have fast speeds. For now, CUDA 8.0 with cuDNN 6.0.20 has been tested. The other versions should be working.
Our proposed Chinese Character dataset is accessible on link
build train/validation/test sets,
1-make_chinese1_list.py
2-make_letter_list.py
3-make_tiny_letter_list.py
4-train_capital_letter_resnet20.py
5-train_chinese1_resnet20.py
6-train_tiny_letter_resnet20.py
7-FARCE_capital_letter.py
8-FARCE_chinese.py
9-FARCE_mnist.py
All experiments were run on NVIDIA TITAN Xp
| model | #GPUs | train time |
|---|---|---|
| train_mnist_resnet20 | 1 | ~10min |
| train_capital_letter_resnet20 | 1 | ~7min |
| train_chinese1_resnet20 | 1 | ~10min |
| FARCE_mnist | 1 | ~20min |
| FARCE_capital_letter | 2 | ~20min |
| FARCE_chinese | 1 | ~20min |