LifeLong-Gan

Requirements

• Tensorflow 2.0.0
• Tensorlayer 2.0
• Python 3.6
• Numpy
• Tqdm

Model

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.