  Instance-incremental learning (IIL) focuses on learning continually with data
of the same classes. Compared to class-incremental learning (CIL), the IIL is
seldom explored because IIL suffers less from catastrophic forgetting (CF).
However, besides retaining knowledge, in real-world deployment scenarios where
the class space is always predefined, continual and cost-effective model
promotion with the potential unavailability of previous data is a more
essential demand. Therefore, we first define a new and more practical IIL
setting as promoting the model's performance besides resisting CF with only new
observations. Two issues have to be tackled in the new IIL setting: 1) the
notorious catastrophic forgetting because of no access to old data, and 2)
broadening the existing decision boundary to new observations because of
concept drift. To tackle these problems, our key insight is to moderately
broaden the decision boundary to fail cases while retain old boundary. Hence,
we propose a novel decision boundary-aware distillation method with
consolidating knowledge to teacher to ease the student learning new knowledge.
We also establish the benchmarks on existing datasets Cifar-100 and ImageNet.
Notably, extensive experiments demonstrate that the teacher model can be a
better incremental learner than the student model, which overturns previous
knowledge distillation-based methods treating student as the main role.
