CLOM

This is the official repository of Continual Learning Based on OOD Detection and Task Masking (CLOM).

Environments

The code has been tested on two different machines with

2x GTX 1080

cuda=10.2
pytorch=1.6.0
torchvision=0.7.0
cudatoolkit=10.2.89
tensorboardx=2.1
apex=0.1
diffdist=0.1
gdown=4.4.0

1x RTX 3090

cuda=11.4
pytorch=1.7.1
torchvision=0.8.2
cudatoolkit=11.0.221
tensorboardx=2.1
diffdist=0.1
gdown=4.4.0

Please install the necessary packages

Training

Please run train_DATASET.sh for a single gpu machine or train_DATASET_multigpu.sh for multi gpu. e.g.

bash train_cifar10.sh

bash train_cifar10_multigpu.sh

For mixed precision, use --amp

Evaluation using pre-trained models

Please download the pre-trained models and calibration parameters by running download_pretrained_models.py or download manually from link. The models and calibration parameters need to be saved under ./logs/DATASET/linear_task_TASK_ID, where DATASET are one of [mnist, cifar10, cifar100_10t, cifar100_20t, tinyImagenet_5t, tinyImageNet_10t] and TASK_ID is the last task id in the experiment (e.g. 9 for cifar100_10t).

For CIL of memory free method CLOM(-c), run the following line

python eval.py --mode cil --dataset cifar10 --model resnet18 --cil_task 4 --printfn 'cil.txt' --all_dataset --disable_cal

For CIL of memory buffer method CLOM, run the following line

python eval.py --mode cil --dataset cifar10 --model resnet18 --cil_task 4 --printfn 'cil.txt' --all_dataset

For TIL, run the following line

python eval.py --mode test_marginalized_acc --dataset cifar10 --model cifar10 --t 4 --all_dataset --printfn 'til.txt'

You may change --dataset, --model, --cil_task for other experiments

Results

The provided pre-trained models give the following results

CIL

	MNIST	CIFAR10	CIFAR100-10T	CIFAR100-20t	T-ImageNet-5T	T-ImageNet-10T
CLOM(-c)	94.73	88.75	62.82	54.74	45.74	47.40
CLOM	96.50	88.62	65.21	58.14	52.53	47.76

TIL

	MNIST	CIFAR10	CIFAR100-10T	CIFAR100-20t	T-ImageNet-5T	T-ImageNet-10T
CLOM(-c)	99.92	98.66	91.88	94.41	68.40	72.20

CLOM and CLOM(-c) are the same as calibration does not affect TIL performance.

Acknowledgement