Enhancing CNN Incremental Learning Capability with an Expanded Network

Shanshan Cai, Zhuwei Xu, Zhichao Huang, Yueru Chen, C.-C. Jay Kuo

Abstract: One fundamental problem of the convolutional neural network (CNN) is catastrophic forgetting, which occurs when new object classes and data are added while the original dataset is not available any more. Training the network only using the new dataset deteriorates the performance with respect to the old dataset. To overcome this problem, we propose an expanded network architecture, called the ExpandNet, to enhance the CNN incremental learning capability. Our solution keeps filters of the original networks on one hand, yet adds additional filters to the convolutional layers as well as the fully connected layers on the other hand. The proposed new architecture does not need any information of the original dataset, and it is trained using the new dataset only. Extensive evaluations based on the CIFAR -10 and the CIFAR -100 datasets show that the proposed method has a slower forgetting rate as compared to several existing incremental learning networks.