  Task-incremental continual learning refers to continually training a model in
a sequence of tasks while overcoming the problem of catastrophic forgetting
(CF). The issue arrives for the reason that the learned representations are
forgotten for learning new tasks, and the decision boundary is destructed.
Previous studies mostly consider how to recover the representations of learned
tasks. It is seldom considered to adapt the decision boundary for new
representations and in this paper we propose a Supervised Contrastive learning
framework with adaptive classification criterion for Continual Learning (SCCL),
In our method, a contrastive loss is used to directly learn representations for
different tasks and a limited number of data samples are saved as the
classification criterion. During inference, the saved data samples are fed into
the current model to obtain updated representations, and a k Nearest Neighbour
module is used for classification. In this way, the extensible model can solve
the learned tasks with adaptive criteria of saved samples. To mitigate CF, we
further use an instance-wise relation distillation regularization term and a
memory replay module to maintain the information of previous tasks. Experiments
show that SCCL achieves state-of-the-art performance and has a stronger ability
to overcome CF compared with the classification baselines.
