Awesome
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.
Dataset | 5K | 10K | 15K | 20K | 25K | 30K | 35K | 40K | 45K | 50K | 55K | 60K |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Flowers | 29.26 | 23.25 | 19.73 | 17.98 | 17.04 | 16.10 | 15.93 | 15.38 | 15.33 | 14.96 | 15.19 | 14.75 |
Cathedrals | 19.78 | 18.32 | 17.10 | 16.47 | 16.15 | 16.33 | 16.08 | 15.94 | 15.78 | 15.60 | 15.64 | 15.67 |
Cats | 38.56 | 25.74 | 23.14 | 21.15 | 20.80 | 20.89 | 19.73 | 19.88 | 18.69 | 18.57 | 17.57 | 18.18 |
For lifelong classification
-
run
classConditionGANMemory.py
for each task until the whole sequeence of tasks are remembered and save the generators; -
run
Lifelong_classification.py
to get the classification results. -
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?.