  Incremental learning is a form of online learning. Incremental learning can
modify the parameters and structure of the deep learning model so that the
model does not forget the old knowledge while learning new knowledge.
Preventing catastrophic forgetting is the most important task of incremental
learning. However, the current incremental learning is often only for one type
of input. For example, if the input images are of the same type, the current
incremental model can learn new knowledge while not forgetting old knowledge.
However, if several categories are added to the input graphics, the current
model will not be able to deal with it correctly, and the accuracy will drop
significantly. Therefore, this paper proposes a kind of incremental method,
which adjusts the parameters of the model by identifying the prototype vector
and increasing the distance of the vector, so that the model can learn new
knowledge without catastrophic forgetting. Experiments show the effectiveness
of our proposed method.
