  Class-incremental learning (CIL) suffers from the notorious dilemma between
learning newly added classes and preserving previously learned class knowledge.
That catastrophic forgetting issue could be mitigated by storing historical
data for replay, which yet would cause memory overheads as well as imbalanced
prediction updates. To address this dilemma, we propose to leverage "free"
external unlabeled data querying in continual learning. We first present a CIL
with Queried Unlabeled Data (CIL-QUD) scheme, where we only store a handful of
past training samples as anchors and use them to query relevant unlabeled
examples each time. Along with new and past stored data, the queried unlabeled
are effectively utilized, through learning-without-forgetting (LwF)
regularizers and class-balance training. Besides preserving model
generalization over past and current tasks, we next study the problem of
adversarial robustness for CIL-QUD. Inspired by the recent success of learning
robust models with unlabeled data, we explore a new robustness-aware CIL
setting, where the learned adversarial robustness has to resist forgetting and
be transferred as new tasks come in continually. While existing options easily
fail, we show queried unlabeled data can continue to benefit, and seamlessly
extend CIL-QUD into its robustified versions, RCIL-QUD. Extensive experiments
demonstrate that CIL-QUD achieves substantial accuracy gains on CIFAR-10 and
CIFAR-100, compared to previous state-of-the-art CIL approaches. Moreover,
RCIL-QUD establishes the first strong milestone for robustness-aware CIL. Codes
are available in https://github.com/VITA-Group/CIL-QUD.
