Awesome

LifeLong-Gan

Requirements

Tensorflow 2.0.0
Tensorlayer 2.0
Python 3.6
Numpy
Tqdm

Model

<div align="center"><img src="img/model.png"></div>

Prepare data

Create a folder data in the project directory.
You can run download_dataset.sh in folder datasets to download the images or directly put the images in folder data, training data in a subdirectory train and test data in val.

Run

Training
```
python train.py --tasks edges2shoes+facades
```
Tasks edges2shoes and facades will be trained in turn. You can replace them with your datasets and concatenate the task names with "+". More training settings can be found in params.py.
Evaluation
```
python evaluate.py --tasks edges2shoes+facades
```
The evaluation results can be found in subdirectory samples.

Reference

[1] Lifelong GAN: Continual Learning for Conditional Image Generation. ICCV, 2019
[2] Toward Multimodal Image-to-Image Translation. NeurIPS, 2017.