  Incremental class learning, a scenario in continual learning context where
classes and their training data are sequentially and disjointedly observed,
challenges a problem widely known as catastrophic forgetting. In this work, we
propose a novel incremental class learning method that can significantly reduce
memory overhead compared to previous approaches. Apart from conventional
classification scheme using softmax, our model bases on an autoencoder to
extract prototypes for given inputs so that no change in its output unit is
required. It stores only the mean of prototypes per class to perform
metric-based classification, unlike rehearsal approaches which rely on large
memory or generative model. To mitigate catastrophic forgetting, regularization
methods are applied on our model when a new task is encountered. We evaluate
our method by experimenting on CIFAR-100 and CUB-200-2011 and show that its
performance is comparable to the state-of-the-art method with much lower
additional memory cost.
