Autoencoder-Based Incremental Class Learning without Retraining on Old Data

Understand

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.