Hyperparameters in Continual Learning: A Reality Check
<|reference_start|>Hyperparameters in Continual Learning: A Reality Check: In this paper, we argue that the conventional evaluation protocol in continual learning (CL) research deviates from the fundamental principle in machine learning evaluation. The primary objective of CL algorithm is to balance the trade-off between plasticity (learning new knowledge from new tasks) and stability (retaining knowledge from previous tasks). To evaluate it, a CL scenario is constructed by using a benchmark dataset, where a neural network model is continually trained on the training data of each task, and the best hyperparameters for a CL algorithm are selected based on validation data.The final evaluation involves assessing the model trained with these hyperparameters on the test data from the same scenario. This evaluation protocol primarily aims to assess how well a CL algorithm performs on unseen data within that specific scenario. However, to accurately evaluate the CL algorithm, the focus should be on assessing generalizability of each algorithm's CL capacity to handle unseen scenarios. To achieve this evaluation goal, we propose a revised evaluation protocol. Our protocol consists of two phases: hyperparameter tuning and evaluation. Both phases share the same scenario configuration (e.g., the number of tasks) but the scenarios for each phase are generated from different datasets. During the hyperparameter tuning phase, the best hyperparameters are identified, which are then used to train the model using the CL algorithm in the evaluation phase. Finally, the result from this phase is reported as the final evaluation. We apply the proposed evaluation protocol to class-incremental learning algorithms, both with and without a pretrained model. Through extensive experiments involving approximately 5000 trials, we demonstrate that most state-of-the-art algorithms fail to exhibit the reported performance, revealing a lack of generalizability.<|reference_end|>
@article{cha2024hyperparameters,
    title={Hyperparameters in Continual Learning: A Reality Check},
    author={Sungmin Cha and Kyunghyun Cho},
    journal={arXiv preprint arXiv:2403.09066},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.09066},
    primaryClass={cs.LG cs.CV}
}