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The official PyTorch code for AAAI'23 Paper "Sparse Coding in a Dual Memory System for Lifelong Learning"

License: MIT License

Python 100.00%
brain-inspired-ai continual-learning deep-learning lifelong-learning sparsity

scommer's Introduction

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

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