  Class-Incremental Learning (CIL) aims to train a reliable model with the
streaming data, which emerges unknown classes sequentially. Different from
traditional closed set learning, CIL has two main challenges: 1) Novel class
detection. The initial training data only contains incomplete classes, and
streaming test data will accept unknown classes. Therefore, the model needs to
not only accurately classify known classes, but also effectively detect unknown
classes; 2) Model expansion. After the novel classes are detected, the model
needs to be updated without re-training using entire previous data. However,
traditional CIL methods have not fully considered these two challenges, first,
they are always restricted to single novel class detection each phase and
embedding confusion caused by unknown classes. Besides, they also ignore the
catastrophic forgetting of known categories in model update. To this end, we
propose a Class-Incremental Learning without Forgetting (CILF) framework, which
aims to learn adaptive embedding for processing novel class detection and model
update in a unified framework. In detail, CILF designs to regularize
classification with decoupled prototype based loss, which can improve the
intra-class and inter-class structure significantly, and acquire a compact
embedding representation for novel class detection in result. Then, CILF
employs a learnable curriculum clustering operator to estimate the number of
semantic clusters via fine-tuning the learned network, in which curriculum
operator can adaptively learn the embedding in self-taught form. Therefore,
CILF can detect multiple novel classes and mitigate the embedding confusion
problem. Last, with the labeled streaming test data, CILF can update the
network with robust regularization to mitigate the catastrophic forgetting.
Consequently, CILF is able to iteratively perform novel class detection and
model update.
