We first pose the Unsupervised Continual Learning (UCL ) problem: learning salient representations from a non-stationary stream of unlabeled data in which the number of object classes varies with time. Given limited labeled data just before inference , those representations can also be associated with specific object types to perform classification . To solve the UCL problem, we propose an architecture that involves a single module, called Self-Taught Associative Memory (STAM) , which loosely models the function of a cortical column in the mammalian brain. 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 .
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 .
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Continual Learning (UCL
Progressive Learning (UPL
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varies
increases
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Given
If some
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just before inference
is also available
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also
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object types to perform classification
classes, thus enabling classification tasks
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UCL
UPL
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a single
an online clustering
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, which loosely models the function of a cortical column in the mammalian brain. Hierarchies
. Layered hierarchies
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Hebbian learning,
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detection of novel patterns
novelty detection
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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
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