A method that utilized Generative Adversarial Network (GAN) to interpret the black-box deep image classifier models by PyTorch.

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Deep Learning FRACE
Overview

Requirements

  1. The project was implemented and tested in Python 3.5 and Pytorch 0.4. The higher versions should work after minor modification.
  2. Other common modules like numpy, pandas and seaborn for visualization.
  3. 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.

Datasets

Our proposed Chinese Character dataset is accessible on link

Implementation details

data preparation

build train/validation/test sets,

1-make_chinese1_list.py
2-make_letter_list.py
3-make_tiny_letter_list.py

training

4-train_capital_letter_resnet20.py
5-train_chinese1_resnet20.py
6-train_tiny_letter_resnet20.py

explanation generation

7-FARCE_capital_letter.py
8-FARCE_chinese.py
9-FARCE_mnist.py

Time and Space

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
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