SHELS: Exclusive Feature Sets For Novelty Detection And Continual Learning Without Class Boundaires

1. Requirements

• pip install -r requirements.txt

  or

• conda create --name <env_name> --file requirements.txt

2. Datsets

• Pytorch datasets for MNIST, FMNIST, SVHN and CIFAR10

• GTSRB can be downloaded here https://sid.erda.dk/public/archives/daaeac0d7ce1152aea9b61d9f1e19370/published-archive.html

  Make sure to set the data_path variable in main.py to the data folder path

3. Novelty detection

create directories to save the models and activations, example mkdir dir dir_bl

MNIST (within-datatset)

train

### shels
 python main.py --dataset1 mnist --ID_tasks 5 --total_tasks 10 --batch_size 32 --lr 0.0001 --epochs 10 --cosine_sim True --sparsity_gs True --train True --random_seed 5 --save_path ./dir

### baseline
 python main.py --dataset1 mnist --ID_tasks 5 --total_tasks 10 --batch_size 32 --lr 0.0001 --epochs 10 --cosine_sim True --baseline True --train True --random_seed 5 --save_path ./dir_bl

evaluation

 ### shels
  python main.py --dataset1 mnist --ID_tasks 5 --total_tasks 10 --batch_size 1 --lr 0.0001 --epochs 10 --cosine_sim True --sparsity_gs True --load_checkpoint True --random_seed 5 --save_path ./dir

 ### baseline
  python main.py --dataset1 mnist --ID_tasks 5 --total_tasks 10 --batch_size 1 --lr 0.0001 --epochs 10 --cosine_sim True --baseline True --load_checkpoint True --random_seed 5 --save_path ./dir_bl --baseline_ood True

To run experiments with different datasets, choose dataset1 argument from [mnist, fmnist, cifar10, svhn, gtsrb].

Note : Be sure to specify the --total_tasks as well as --ID_tasks arguments, total number of classes and total number of ID classes respectively

MNIST (ID) vs FMNIST (OOD) (across-datasets)

train

### shels
 python main.py --dataset1 mnist --dataset2 fmnist --multiple_dataset True --ID_tasks 10 --total_tasks 10 --batch_size 32 --lr 0.0001 --epochs 10 --cosine_sim True --sparsity_gs True --train True --save_path ./dir

### baseline
 python main.py --dataset1 mnist --dataset2 fmnist --multiple_dataset True --ID_tasks 10 --total_tasks 10 --batch_size 32 --lr 0.0001 --epochs 10 --cosine_sim True --baseline True --train True --save_path ./dir_bl

evaluation

### shels
 python main.py --dataset1 mnist --dataset2 fmnist --multiple_dataset True --ID_tasks 10 --total_tasks 10 --batch_size 1 --lr 0.0001 --epochs 10 --cosine_sim True --sparsity_gs True --load_checkpoint True --save_path ./dir

### baseline
 python main.py --dataset1 mnist --dataset2 fmnist --multiple_dataset True --ID_tasks 10 --total_tasks 10 --batch_size 1 --lr 0.0001 --epochs 10 --cosine_sim True --baseline True --load_checkpoint True --save_path ./dir_bl --baseline_ood True

To run experiments with different datasets, choose dataset1 and dataset2 from [mnist, fmnist, cifar10, svhn, gtsrb]

Note : Be sure to specify the --total_tasks as well as --ID_tasks arguments and ensure cosistent input dimension in data_loader.py for ID and OOD datasets

4. Novelty Accommodation and Novelty detection-Accommodation

MNIST

train

 python main.py --dataset1 mnist --ID_tasks 7 --total_tasks 10 --batch_size 32 --lr 0.0001 --epochs 10 --cosine_sim True --sparsity_gs True --train True --random_seed 5 --save_path ./dir

accommodation

python main.py --dataset1 mnist --ID_tasks 7 --total_tasks 10 --batch_size 1 --lr 0.0001 --epochs 10 --cosine_sim True --sparsity_gs True --load_checkpoint True --random_seed 5 --save_path ./dir --cont_learner True

detection and accommodation

python main.py --dataset1 mnist --ID_tasks 7 --total_tasks 10 --batch_size 1 --lr 0.0001 --epochs 10 --cosine_sim True --sparsity_gs True --load_checkpoint True --random_seed 5 --full_pipeline True --save_path ./dir --cont_learner True

To run experiments with different datasets, choose dataset1 from [mnist, fmnist, cifar10, svhn, gtsrb] To load a preloaded set of experiments, use class_list_GTSRB.npz for GTSRB dataset and class_list1.npz for the other datasets by setting--load_list flag to True.

Citing this work

If you use this work please cite our paper.

@inproceedings{gummadi2022shels
 title = {SHELS: Exclusive Feature Sets for Novelty Detection and Continual Learning Without Class Boundaries},
 authors = {Gummadi, Meghna and Kent, David and Mendez, Jorge A. and Eaton, Eric},
 booktitle = {1st Conference on Lifelong Learning Agents (CoLLAs-22)},
 year = {2022}
}