- Python 3
- Cifar100/Cifar10/100 : GPU 1080Ti / Pytorch 1.3.1+cu9.2 / CUDA 9.2
- Omniglot : GPU Titan RTX / Pytorch 1.3.1 / CUDA 10.0
- CUB200 : GPU 1080Ti / Pytorch 1.0.0+cu9.2 / CUDA 9.2
- Omniglot : https://drive.google.com/file/d/1WxFZQyt3v7QRHwxFbdb1KO02XWLT0R9z/view?usp=sharing
- CUB200 : https://github.com/visipedia/tf_classification/wiki/CUB-200-Image-Classification
# Cifar100
$ python3 ./main.py --experiment split_cifar100 --approach gs --lamb 400 --mu 10 --rho 0.3 --eta 0.9
# Cifar10/100
$ python3 ./main.py --experiment split_cifar10_100 --approach gs --lamb 7000 --mu 20 --rho 0.2 --eta 0.9
# Omniglot
$ python3 ./main.py --experiment omniglot --approach gs --lamb 1000 --mu 7 --rho 0.5 --eta 0.9
# CUB200
$ cd LargeScale_AGS
$ python3 ./main.py --dataset CUB200 --trainer gs --lamb 1.5 --mu 0.5 --rho 0.1 --eta 0.9
| CIFAR100 | CIFAR-10/100 | Omniglot | CUB-200 | |
|---|---|---|---|---|
| AGS-CL | 64.2 | 76.0 | 82.2 | 82.9 |
- Python 3.6
- Pytorch 1.2.0+cu9.2 / CUDA 9.2
- OpenAI Gym, Baselines
The experimental environment for reinforcement learning is built based on pytorch-a2c-ppo-acktr-gaail
Follow below links for installation
# Fine-tuning
$ CUDA_VISIBLE_DEVICES=0 python3 main_rl.py --approach 'fine-tuning' --seed 0 --date 200605
# EWC
$ CUDA_VISIBLE_DEVICES=0 python3 main_rl.py --approach 'ewc' --seed 0 --date 200605
# AGS-CL
$ CUDA_VISIBLE_DEVICES=0 python3 main_rl.py --approach 'gs' --seed 0 --date 200605 --gs-mu 0.1 --gs-lamb 1000