Hyperparameters in Continual Learning: A Reality Check

A recent study on continual learning (CL) highlights the importance of hyperparameters in training models across various tasks. The research emphasizes the balance between learning new information and retaining existing knowledge, which is crucial for the effectiveness of CL algorithms. By examining conventional evaluation protocols, the study aims to provide insights that could enhance the performance of machine learning models in dynamic environments. This matters because as technology evolves, the ability to adapt and learn continuously is becoming increasingly vital for AI applications.