Modeling Label Delay in Online Continual Learning

Continual learning models often struggle when the data distribution changes over time, and this challenge is exacerbated when there is a delay between the arrival of new data and the corresponding labels due to slow annotation processes. This paper proposes a new continual learning framework that explicitly models this label delay and explores methods to effectively utilize the unlabeled data to bridge the performance gap caused by the delay.