Class Incremental Learning via Feature Knowledge Prompts

Incremental learning uses previous knowledge and experience to deal with new problems. Traditional class incremental Learning methods usually need to ensure the current model's sensitivity to old tasks by storing, retrieving and using a large amount of historical data, which suffers from difficulties such as high storage costs and high dependence on data availability. This work proposes a class incremental learning method based on feature knowledge prompts to help models better select and utilize old knowledge elements to assist model training. The design contains prompt matching sample feature maps related to specific fields. A feature splitting method is used to isolate the common and special features of a sample to indicate the potential feature space direction of the sample to be tested. The method has been verified to have outstanding performance through multiple data sets and multiple tasks.