  While many works on Continual Learning have shown promising results for
mitigating catastrophic forgetting, they have relied on supervised training. To
successfully learn in a label-agnostic incremental setting, a model must
distinguish between learned and novel classes to properly include samples for
training. We introduce a novelty detection method that leverages network
confusion caused by training incoming data as a new class. We found that
incorporating a class-imbalance during this detection method substantially
enhances performance. The effectiveness of our approach is demonstrated across
a set of image classification benchmarks: MNIST, SVHN, CIFAR-10, CIFAR-100, and
CRIB.
