  Adversarial continual learning is effective for continual learning problems
because of the presence of feature alignment process generating task-invariant
features having low susceptibility to the catastrophic forgetting problem.
Nevertheless, the ACL method imposes considerable complexities because it
relies on task-specific networks and discriminators. It also goes through an
iterative training process which does not fit for online (one-epoch) continual
learning problems. This paper proposes a scalable adversarial continual
learning (SCALE) method putting forward a parameter generator transforming
common features into task-specific features and a single discriminator in the
adversarial game to induce common features. The training process is carried out
in meta-learning fashions using a new combination of three loss functions.
SCALE outperforms prominent baselines with noticeable margins in both accuracy
and execution time.
