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, learning 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, forgetting outliers, and storing only
prototypical features rather than specific examples. We evaluate STAM
representations using clustering and classification tasks. While there are no
existing learning scenarios that are directly comparable to UPL, we compare the
STAM architecture with two recent continual learning models, Memory Aware
Synapses (MAS) and Gradient Episodic Memories (GEM), after adapting them in the
UPL setting.

本文提出了一种解决自主学习和非稳态无标签数据流问题的方法：使用一种自学习联想存储体结构和在线聚类，新颖性检测以及仅存储典型特征等方法，实现动态增长的特征数并解决异常点问题，同时该方案也在聚类和分类任务上进行了评估。