Before revision:  Hierarchies of STAM modules learn based on a combination of Hebbian learning, online clustering, detection of novel patterns , URLetting outliers, and top-down predictions. We illustrate the operation of STAMs in the context of learning handwritten digits in a continual manner with only 3-12 labeled examplesper class. STAMs suggest a promising direction to solve the UCL problemwithout catastrophic URLetting .
After revision:  Hierarchies of STAM modules learn based on a combination of Hebbian learning, online clustering, detection of novel patterns , 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 .