Dynamic Model Merging for Class-Incremental Learning on Imbalanced Ocular Disease Image Dataset

This paper presents an implementation of Class-Incremental Learning on an imbalanced subset of the RFMiD dataset, utilizing the Dynamic Model Merging (DynaMMo) technique and Class-Balanced Focal Loss.