SCoMMER

Official Repository for the (Oral) AAAI'23 paper Sparse Coding in a Dual Memory System for Lifelong Learning

We extended the CLS-ER repo with our method

Setup

• Use python main.py to run experiments.

• Use argument --load_best_args to use the best hyperparameters for each of the evaluation setting from the paper.

• To reproduce the results in the paper run the following

  python main.py --dataset <dataset> --model <model> --experiment_id <experiment_id> --buffer_size <buffer_size> --load_best_args

  Examples:

  python main.py --dataset seq-cifar10 --model scommer --buffer_size 200 --experiment_id scommer-c10-200 --load_best_args
  
  python main.py --dataset seq-cifar100 --model scommer --buffer_size 200 --experiment_id scommer-c100-200 --load_best_args
  

  For GCIL-CIFAR-100 Experiments

  python main.py --dataset gcil-cifar100 --weight_dist unif --model scommer --buffer_size 200 --experiment_id scommer-gcil-unif-200 --load_best_args
  
  python main.py --dataset gcil-cifar100 --weight_dist longtail --model scommer --buffer_size 200 --experiment_id scommer-gcil-longtail-200 --load_best_args
  

Requirements

• torch==1.7.0

• torchvision==0.9.0

• quadprog==0.1.7