  Human beings are able to master a variety of knowledge and skills with
ongoing learning. By contrast, dramatic performance degradation is observed
when new tasks are added to an existing neural network model. This phenomenon,
termed as \emph{Catastrophic Forgetting}, is one of the major roadblocks that
prevent deep neural networks from achieving human-level artificial
intelligence. Several research efforts, e.g. \emph{Lifelong} or
\emph{Continual} learning algorithms, have been proposed to tackle this
problem. However, they either suffer from an accumulating drop in performance
as the task sequence grows longer, or require to store an excessive amount of
model parameters for historical memory, or cannot obtain competitive
performance on the new tasks. In this paper, we focus on the incremental
multi-task image classification scenario. Inspired by the learning process of
human students, where they usually decompose complex tasks into easier goals,
we propose an adversarial feature alignment method to avoid catastrophic
forgetting. In our design, both the low-level visual features and high-level
semantic features serve as soft targets and guide the training process in
multiple stages, which provide sufficient supervised information of the old
tasks and help to reduce forgetting. Due to the knowledge distillation and
regularization phenomenons, the proposed method gains even better performance
than finetuning on the new tasks, which makes it stand out from other methods.
Extensive experiments in several typical lifelong learning scenarios
demonstrate that our method outperforms the state-of-the-art methods in both
accuracies on new tasks and performance preservation on old tasks.
