We first pose the Unsupervised Progressive Learning (UPL) problem: learning salient representations from a non-stationary stream of unlabeled data in which the number of object classes increases with time . To solve the UPL problem we propose an architecture that involves a module called Self-Taught Associative Memory (STAM)  . Layered hierarchies of STAM modules learn based on a combination of online clustering, novelty detection, URLetting outliers, and storing only prototypical representations rather than specific examples. We evaluate STAM representations using clustering and classification tasks, relying on limited labeled data for the latter . Even though there are no prior approaches that are directly applicable to the UPL problem, we compare the STAM architecture to a couple of unsupervised and self-supervised deep learning approaches adapted in the UPL context.
We first pose the Unsupervised Progressive Learning (UPL) problem: an online representation learning problem in which the learner observes a non-stationary and unlabeled data stream, and identifies a growing number of features that persist over time even though the data is not stored or replayed . To solve the UPL problem we propose the Self-Taught Associative Memory (STAM) architecture . Layered hierarchies of STAM modules learn based on a combination of online clustering, novelty detection, URLetting outliers, and storing only prototypical features rather than specific examples. We evaluate STAM representations using classification and clustering tasks . Even though there are no prior approaches that are directly applicable to the UPL problem, we evaluate the STAM architecture in comparison to some unsupervised and self-supervised deep learning approaches adapted in the UPL context.
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learning salient representations from
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clustering and classification tasks, relying on limited labeled data for the latter
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