CLASS INCREMENTAL LEARNING WITH TASK-SELECTION

Despite the success of the deep neural networks (DNNs), in case of incremental learning, DNNs are known to suffer from catastrophic forgetting problems which are the phenomenon of entirely forgetting previously learned task information upon learning current task information. To alleviate this problem, we propose a novel knowledge distillation-based class incremental learning method with a task-selective autoencoder (TsAE). By learning the TsAE to reconstruct the feature map of each task, the proposed method effectively memorizes not only the classes of the current task but also the classes of previously learned tasks. Since the proposed TsAE has a simple but powerful architecture, it can be easily generalized to other knowledge distillation-based class incremental learning methods. Our experimental results on various datasets, including iCIFAR-100 and iILSVRC-small, demonstrated that the proposed method achieves higher classification accuracy and less forgetting compared to the state-of-the-art methods.