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Continual Learning: Evaluating Hyperparameters for CL Algorithms

Core Concepts
The evaluation protocol for continual learning algorithms should involve Hyperparameter Tuning and Evaluation phases to accurately assess their CL capability.
Various CL algorithms aim to balance stability and plasticity during the learning process. The current evaluation protocol involves tuning hyperparameters on benchmark datasets, leading to overfitting and impracticality. Proposed protocol includes two phases: Hyperparameter Tuning and Evaluation, using different datasets but the same CL scenario. Experimental results show that some state-of-the-art algorithms exhibit inferior performance compared to older ones in the proposed protocol. Additional analysis on model size and training time reveals efficiency issues in certain algorithms despite better CL capacity.
"In recent years, extensive research has been conducted on continual learning (CL) to effectively adapt to successive novel tasks while overcoming catastrophic forgetting for previous tasks [23]." "Various CL algorithms tailored for successful CL in classification offer novel approaches to balance stability and plasticity during the CL process." "Despite differing approaches in these three categories, they inevitably require introducing additional hyperparameters for their algorithm."
"Returning to the fundamental principles of model evaluation in machine learning, we propose an evaluation protocol that involves Hyperparameter Tuning and Evaluation phases." "This highlights the necessity of adopting the proposed protocol for a more comprehensive evaluation of CL algorithms."

Key Insights Distilled From

by Sungmin Cha,... at 03-15-2024
Hyperparameters in Continual Learning

Deeper Inquiries

How can evaluating hyperparameters impact the generalizability of continual learning algorithms

ハイパーパラメータを評価することは、継続的学習アルゴリズムの汎化性能にどのような影響を与えるでしょうか? Answer 1 here

What are the implications of overfitting hyperparameters on real-world applications of continual learning

ハイパーパラメータの過剰適合が継続的学習の実世界応用に与える影響は何ですか? Answer 2 here

How can the proposed evaluation protocol be extended to other domains beyond class-incremental learning

提案された評価プロトコルは、クラス増分学習以外の領域にどのように拡張できますか? Answer 3 here