Online Class Incremental Learning (OCIL) aims to train the model in a
task-by-task manner, where data arrive in mini-batches at a time while previous
data are not accessible. A significant challenge is known as Catastrophic
Forgetting, i.e., loss of the previous knowledge on old data. To address this,
replay-based methods show competitive results but invade data privacy, while
exemplar-free methods protect data privacy but struggle for accuracy. In this
paper, we proposed an exemplar-free approach -- Analytic Online Class
Incremental Learning (AOCIL). Instead of back-propagation, we design the
Analytic Classifier (AC) updated by recursive least square, cooperating with a
frozen backbone. AOCIL simultaneously achieves high accuracy, low resource
consumption and data privacy protection. We conduct massive experiments on four
existing benchmark datasets, and the results demonstrate the strong capability
of handling OCIL scenarios. Codes will be ready.

分析型在线课堂增量学习（AOCIL）是一个无需示例的方法，通过递归最小二乘法更新分析分类器（AC）实现高准确性、低资源消耗和数据隐私保护，能够处理 OCIL 情景。