Reinforcing Neuron Extraction From Calcium Imaging Data Via Depth-Estimation Constrained Nonnegative Matrix Factorization

Continual learning aims to continuously learn new tasks from new data while retaining the knowledge of tasks learned in the past. Recently, the Vision Transformer, which utilizes the Transformer initially proposed in natural language processing for computer vision, has shown higher accuracy than Convolutional Neural Networks (CNN) in image recognition tasks. However, there are few methods that have achieved continual learning with Vision Transformer. in this paper, we compare and improve continual learning methods that can be applied to both CNN and Vision Transformers. in our experiments, we compare several continual learning methods and their combinations to show the differences in accuracy and the number of parameters.