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Incremental class learning, a scenario in continual learning context whereclasses and their training data are sequentially and disjointedly observed,challenges a problem widely known as catastrophic forgetting. In this work, wepropose a novel incremental class learning method that can significantly reducememory overhead compared to previous approaches. Apart from conventionalclassification scheme using softmax, our model bases on an autoencoder toextract prototypes for given inputs so that no change in its output unit isrequired. It stores only the mean of prototypes per class to performmetric-based classification, unlike rehearsal approaches which rely on largememory or generative model. To mitigate catastrophic forgetting, regularizationmethods are applied on our model when a new task is encountered. We evaluateour method by experimenting on CIFAR-100 and CUB-200-2011 and show that itsperformance is comparable to the state-of-the-art method with much loweradditional memory cost.

Autoencoder-Based Incremental Class Learning without Retraining on Old Data