A self-organizing incremental neural network for continual supervised learning

Continual learning algorithms can adapt to changes of data distributions, new classes, and even completely new tasks without catastrophically forgetting previously acquired knowledge. Here, we present a novel self-organizing incremental neural network, GSOINN+, for continual supervised learning. GSOINN+ learns a topological mapping of the input data to an undirected network and uses a weighted nearest-neighbor rule with fractional distance for classification. GSOINN+ learns incrementally-new classification tasks do not need to be specified a priori, and no rehearsal of previously learned tasks with stored training sets is required. In a series of sequential learning experiments, we show that GSOINN+ can mitigate catastrophic forgetting, even when completely new tasks are to be learned.