AOCIL: Exemplar-free Analytic Online Class Incremental Learning with Low
  Time and Resource Consumption
Huiping Zhuang Yuchen Liu Run He Kai Tong Ziqian Zeng Cen Chen Yi Wang Lap-Pui Chau
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.