Continual Learning Model with Dynamic Adaptation and Flexibility for Incremental Classes

In recent years, deep learning models have achieved highly accurate image classification. However, in scenarios where the number of target classes continues to increase while using a model, it is not very effective to retrain the model by using all data from the beginning each time. Consequently, there has been increasing interest in class-incremental learning, which sequentially updates existing models with newly introduced classes. In particular, Forward Compatible Few-Shot Class-Incremental Learning (FACT) has demonstrated high classification performance by proactively reserving regions within the embedding space to represent knowledge of classes that will be added in the future. In this study, we propose FACT+, which seeks to further enhance the performance of FACT by incorporating two key extensions. First, to account for the semantic diversity (i.e., the breadth or narrowness of meaning) of each class, we introduce variability into each class’s covariance matrix. Second, to address the issue of insufficient learning from incremental data, we implement a mechanism to continuously update the model’s knowledge through the reuse of data collected during deployment. Experimental results show that the proposed method outperforms FACT in terms of classification accuracy and confirm the validity of the embedding space learned by the proposed approach.