  Incremental learning suffers from two challenging problems; forgetting of old
knowledge and intransigence on learning new knowledge. Prediction by the model
incrementally learned with a subset of the dataset are thus uncertain and the
uncertainty accumulates through the tasks by knowledge transfer. To prevent
overfitting to the uncertain knowledge, we propose to penalize confident
fitting to the uncertain knowledge by the Maximum Entropy Regularizer (MER).
Additionally, to reduce class imbalance and induce a self-paced curriculum on
new classes, we exclude a few samples from the new classes in every mini-batch,
which we call DropOut Sampling (DOS). We further rethink evaluation metrics for
forgetting and intransigence in incremental learning by tracking each sample's
confusion at the transition of a task since the existing metrics that compute
the difference in accuracy are often misleading. We show that the proposed
method, named 'MEDIC', outperforms the state-of-the-art incremental learning
algorithms in accuracy, forgetting, and intransigence measured by both the
existing and the proposed metrics by a large margin in extensive empirical
validations on CIFAR100 and a popular subset of ImageNet dataset
(TinyImageNet).
