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. If some limited labeled data is also available, those representations can be associated with specific classes, thus enabling classification tasks. To solve the UPL problem , we propose an architecture that involves an online clustering 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. The goal of this paper is to introduce the UPL problem, describe the STAM architecture , and evaluate the latter in the UPL context.
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.
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