  The goal of continual learning is to provide intelligent agents that are
capable of learning continually a sequence of tasks using the knowledge
obtained from previous tasks while performing well on prior tasks. However, a
key challenge in this continual learning paradigm is catastrophic forgetting,
namely adapting a model to new tasks often leads to severe performance
degradation on prior tasks. Current memory-based approaches show their success
in alleviating the catastrophic forgetting problem by replaying examples from
past tasks when new tasks are learned. However, these methods are infeasible to
transfer the structural knowledge from previous tasks i.e., similarities or
dissimilarities between different instances. Furthermore, the learning bias
between the current and prior tasks is also an urgent problem that should be
solved. In this work, we propose a new method, named Online Continual Learning
via the Knowledge Invariant and Spread-out Properties (OCLKISP), in which we
constrain the evolution of the embedding features via Knowledge Invariant and
Spread-out Properties (KISP). Thus, we can further transfer the inter-instance
structural knowledge of previous tasks while alleviating the forgetting due to
the learning bias. We empirically evaluate our proposed method on four popular
benchmarks for continual learning: Split CIFAR 100, Split SVHN, Split CUB200
and Split Tiny-Image-Net. The experimental results show the efficacy of our
proposed method compared to the state-of-the-art continual learning algorithms.
