  With the explosion of digital data in recent years, continuously learning new
tasks from a stream of data without forgetting previously acquired knowledge
has become increasingly important. In this paper, we propose a new continual
learning (CL) setting, namely ``continual representation learning'', which
focuses on learning better representation in a continuous way. We also provide
two large-scale multi-step benchmarks for biometric identification, where the
visual appearance of different classes are highly relevant. In contrast to
requiring the model to recognize more learned classes, we aim to learn feature
representation that can be better generalized to not only previously unseen
images but also unseen classes/identities. For the new setting, we propose a
novel approach that performs the knowledge distillation over a large number of
identities by applying the neighbourhood selection and consistency relaxation
strategies to improve scalability and flexibility of the continual learning
model. We demonstrate that existing CL methods can improve the representation
in the new setting, and our method achieves better results than the
competitors.
