E²Net: Resource-Efficient Continual Learning with Elastic Expansion Network

Introduction

This is the training and evaluation code for our work "E²Net: Resource-Efficient Continual Learning with Elastic Expansion Network".

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> --buffer_size <buffer_size> --load_best_args

Examples:

python main.py --dataset seq-cifar10 --model e2n --buffer_size 500 --load_best_args

python main.py --dataset seq-tinyimg --model e2n --buffer_size 500 --load_best_args

python main.py --dataset seq-cifar100 --model e2n --buffer_size 500 --load_best_args

Requirements

• torch==1.12.1

• torchvision==0.13.1

• tensorflow 2.11.0