  Class-Incremental Learning aims to update a deep classifier to learn new
categories while maintaining or improving its accuracy on previously observed
classes. Common methods to prevent forgetting previously learned classes
include regularizing the neural network updates and storing exemplars in
memory, which come with hyperparameters such as the learning rate,
regularization strength, or the number of exemplars. However, these
hyperparameters are usually only tuned at the start and then kept fixed
throughout the learning sessions, ignoring the fact that newly encountered
tasks may have varying levels of novelty or difficulty. This study investigates
the necessity of hyperparameter `adaptivity' in Class-Incremental Learning: the
ability to dynamically adjust hyperparameters such as the learning rate,
regularization strength, and memory size according to the properties of the new
task at hand. We propose AdaCL, a Bayesian Optimization-based approach to
automatically and efficiently determine the optimal values for those parameters
with each learning task. We show that adapting hyperpararmeters on each new
task leads to improvement in accuracy, forgetting and memory. Code is available
at https://github.com/ElifCerenGokYildirim/AdaCL.
