  Human intelligence gradually accepts new information and accumulates
knowledge throughout the lifespan. However, deep learning models suffer from a
catastrophic forgetting phenomenon, where they forget previous knowledge when
acquiring new information. Class-Incremental Learning aims to create an
integrated model that balances plasticity and stability to overcome this
challenge. In this paper, we propose a selective regularization method that
accepts new knowledge while maintaining previous knowledge. We first introduce
an asymmetric feature distillation method for old and new classes inspired by
cognitive science, using the gradient of classification and knowledge
distillation losses to determine whether to perform pattern completion or
pattern separation. We also propose a method to selectively interpolate the
weight of the previous model for a balance between stability and plasticity,
and we adjust whether to transfer through model confidence to ensure the
performance of the previous class and enable exploratory learning. We validate
the effectiveness of the proposed method, which surpasses the performance of
existing methods through extensive experimental protocols using CIFAR-100,
ImageNet-Subset, and ImageNet-Full.

Computer Vision and Pattern Recognition