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GAN Memory for Lifelong learning

This is a pytorch implementation of the NeurIPS paper GAN Memory with No Forgetting.

Please consider citing our paper if you refer to this code in your research.

@article{cong2020gan,
  title={GAN Memory with No Forgetting},
  author={Cong, Yulai and Zhao, Miaoyun and Li, Jianqiao and Wang, Sijia and Carin, Lawrence},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  year={2020}
}

Requirement

python=3.7.3
pytorch=1.2.0

Notes

The source model is based on the GP-GAN.

GANMemory_Flowers.py is the implementation of the model in Figure1(a).

classConditionGANMemory.py is the class-conditional generalization of GAN memory, which is used as pseudo rehearsal for a lifelong classification as shown in Section 5.2.

Lifelong_classification.py is the code for the lifelong classification part as shown in Section 5.2.

Usage

First, download the pretrained GP-GAN model by running download_pretrainedGAN.py. Note please change the path therein.

Second, download the training data to the folder ./data/. For example, download the Flowers dataset from: https://www.robots.ox.ac.uk/~vgg/data/flowers/102/ to the folder ./data/102flowers/.

Dataset preparation

data
├──102flowers
           ├──all8189images
├── CelebA
...

Finally, run GANMemory_Flowers.py.

The FID scores of our method shown in Figure 1(b) are summerized in the following table.

Dataset5K10K15K20K25K30K35K40K45K50K55K60K
Flowers29.2623.2519.7317.9817.0416.1015.9315.3815.3314.9615.1914.75
Cathedrals19.7818.3217.1016.4716.1516.3316.0815.9415.7815.6015.6415.67
Cats38.5625.7423.1421.1520.8020.8919.7319.8818.6918.5717.5718.18

For lifelong classification

  1. run classConditionGANMemory.py for each task until the whole sequeence of tasks are remembered and save the generators;

  2. run Lifelong_classification.py to get the classification results.

  3. run Compression_low_rank_six_butterfly.py to get the compression results.

Note, for the sake of simplicity, we devide the pseudo rehearsal based lifelong classification processes into above two stages, one can of course find a way to merge these two stages to form a learning process along task sequence.

Acknowledgement

Our code is based on GAN_stability: https://github.com/LMescheder/GAN_stability from the paper Which Training Methods for GANs do actually Converge?.