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We first pose the Unsupervised Progressive Learning (UPL) problem: an onlinerepresentation learning problem in which the learner observes a non-stationaryand unlabeled data stream, learning a growing number of features that persistover time even though the data is not stored or replayed. To solve the UPLproblem we propose the Self-Taught Associative Memory (STAM) architecture.Layered hierarchies of STAM modules learn based on a combination of onlineclustering, novelty detection, forgetting outliers, and storing only prototypicalfeatures rather than specific examples. We evaluate STAM representations usingclustering and classification tasks. While there are no existing learning scenariosthat are directly comparable to UPL, we compare the STAM architecture with tworecent continual learning models, Memory Aware Synapses (MAS) and GradientEpisodic Memories (GEM), after adapting them in the UPL setting.

Unsupervised Progressive Learning and the STAM Architecture