  Lifelong learning with deep neural networks is well-known to suffer from
catastrophic forgetting: the performance on previous tasks drastically degrades
when learning a new task. To alleviate this effect, we propose to leverage a
large stream of unlabeled data easily obtainable in the wild. In particular, we
design a novel class-incremental learning scheme with (a) a new distillation
loss, termed global distillation, (b) a learning strategy to avoid overfitting
to the most recent task, and (c) a confidence-based sampling method to
effectively leverage unlabeled external data. Our experimental results on
various datasets, including CIFAR and ImageNet, demonstrate the superiority of
the proposed methods over prior methods, particularly when a stream of
unlabeled data is accessible: our method shows up to 15.8% higher accuracy and
46.5% less forgetting compared to the state-of-the-art method. The code is
available at https://github.com/kibok90/iccv2019-inc.
