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Do Pre-trained Models Benefit Equally in Continual Learning?


This repository is an official implementation of Do Pre-trained Models Benefit Equally in Continual Learning? in Pytorch (WACV 2023) [Paper][Project Website]

Abstract

Existing work on continual learning (CL) is primarily devoted to developing algorithms for models trained from scratch. Despite their encouraging performance on contrived benchmarks, these algorithms show dramatic performance drop in real-world scenarios. Therefore, this paper advocates the systematic introduction of pre-training to CL, which is a general recipe for transferring knowledge to downstream tasks but is substantially missing in the CL community. Our investigation reveals the multifaceted complexity of exploiting pre-trained models for CL, along three different axes: pre-trained models, CL algorithms, and CL scenarios. Perhaps most intriguingly, improvements in CL algorithms from pre-training are very inconsistent -- an underperforming algorithm could become competitive and even state of the art, when all algorithms start from a pre-trained model. This indicates that the current paradigm, where all CL methods are compared in from-scratch training, is not well reflective of the true CL objective and desired progress. In addition, we make several other important observations, including that 1) CL algorithms that exert less regularization benefit more from a pre-trained model; and 2) a stronger pre-trained model such as CLIP does not guarantee a better improvement. Based on these findings, we introduce a simple yet effective baseline that employs minimum regularization and leverages the more beneficial pre-trained model, coupled with a two-stage training pipeline. We recommend including this strong baseline in the future development of CL algorithms, due to its demonstrated state-of-the-art performance.

Contribution

Requirement

Installing packages

pip install -r requirements.txt

Datasets

Data preparation

Duplicate results

cd scripts; bash run_all.sh pretrained_rn18 1000 cifar100 20 0

Citation

If you use this paper/code in your research, please consider citing us:

Do Pre-trained Models Benefit Equally in Continual Learning?

@InProceedings{Lee_2023_WACV,
    author    = {Lee, Kuan-Ying and Zhong, Yuanyi and Wang, Yu-Xiong},
    title     = {Do Pre-Trained Models Benefit Equally in Continual Learning?},
    booktitle = {Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)},
    month     = {January},
    year      = {2023},
    pages     = {6485-6493}
}

Acknowledgments

This repository is built upon Online Continual Learning.