2 research outputs found
Adaptive Regularization for Class-Incremental Learning
Class-Incremental Learning updates a deep classifier with new categories
while maintaining the previously observed class accuracy. Regularizing the
neural network weights is a common method to prevent forgetting previously
learned classes while learning novel ones. However, existing regularizers use a
constant magnitude throughout the learning sessions, which may not reflect the
varying levels of difficulty of the tasks encountered during incremental
learning. This study investigates the necessity of adaptive regularization in
Class-Incremental Learning, which dynamically adjusts the regularization
strength according to the complexity of the task at hand. We propose a Bayesian
Optimization-based approach to automatically determine the optimal
regularization magnitude for each learning task. Our experiments on two
datasets via two regularizers demonstrate the importance of adaptive
regularization for achieving accurate and less forgetful visual incremental
learning