This code is the official implementation of our paper.
- Python 3
- GPU 1080Ti / Pytorch 1.3.1+cu9.2 / CUDA 9.2
- CUB-200-2011 : https://github.com/visipedia/tf_classification/wiki/CUB-200-Image-Classification
- Tiny-ImageNet :
cd downloadsource download_tinyimgnet.sh
Please download the datasets and put them into ./dat/CUB_200_2011 and ./dat/tiny-imagenet-200, respectively.
The commands to run most of the experiments are included in script_classification.sh.
Below, we present the demo commands for our method with EWC as the default continual learner.
For small-scale images:
# CIFAR-100-SC
$ python3 ./main.py --experiment split_cifar100_sc_5 --approach ewc_coscl --lamb 40000 --lamb1 0.02 --use_TG --s_gate 100 --seed 0
# CIFAR-100-RS
$ python3 ./main.py --experiment split_cifar100_rs_5 --approach ewc_coscl --lamb 10000 --lamb1 0.02 --use_TG --s_gate 100 --seed 0
For large-scale images:
$ cd LargeScale_Image
# CUB-200-2011
$ python3 ./main.py --dataset CUB200 --trainer ewc_coscl --lamb 1 --lamb1 0.0001 --s_gate 100 --tasknum 10 --seed 0
# Tiny-ImageNet
$ python3 ./main.py --dataset tinyImageNet --trainer ewc_coscl --lamb 320 --lamb1 0.001 --s_gate 100 --tasknum 10 --seed 0