  Few-shot class-incremental learning (FSCIL) has addressed challenging
real-world scenarios where unseen novel classes continually arrive with few
samples. In these scenarios, it is required to develop a model that recognizes
the novel classes without forgetting prior knowledge. In other words, FSCIL
aims to maintain the base performance and improve the novel performance
simultaneously. However, there is little study to investigate the two
performances separately. In this paper, we first decompose the entire model
into four types of parameters and demonstrate that the tendency of the two
performances varies greatly with the updated parameters when the novel classes
appear. Based on the analysis, we propose a simple method for FSCIL, coined as
NoNPC, which uses normalized prototype classifiers without further training for
incremental novel classes. It is shown that our straightforward method has
comparable performance with the sophisticated state-of-the-art algorithms.
