ClusterValidityIndices.jl is a Julia package for evaluating the performance of clustering algorithms without the aid of supervised labels. Cluster Validity Indices (CVI) provide a metric of the over-or under-partitioning of an arbitrary clustering algorithm with only the original data and labels assigned by the clustering algorithm. Furthermore, there exist formulations of every CVI such that they may run incrementally (i.e. Incremental CVIs, or ICVI), streaming alongside the clustering algorithm and producing the same results as in their batch implementations. Using a standard interface, each CVI in this package can be run with any clustering algorithm to produce a metric of that algorithm's performance in scenarios where explicit supervised labels do not exist, which is extremely useful in real-world applications where that is often the case.

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<p>Context recognition for lifelong learning (L2) agents is an open-ended problem whereby aggregate features in an environment are utilized to signal the active context in which the agent is operating. The ability to recognize context is necessary in L2 agents to engage modulatory signals to account for significant changes in the input state space associated with a given context or task, such as altering learning dynamics or shifting attention to more relevant features. Context recognition is itself an L2 problem due to the ever-increasing number of distinct contexts that an agent might encounter, requiring incrementally learning novel contexts while prescribing them to supervised task labels when available. This paper demonstrates an algorithm based on near clustering of deep-extracted features with adaptive resonance theory methods that satisfies these requirements on the behalf of an embodied L2 agent in a computer vision environment. The strength of this algorithm lies in its flexibility, being capable of online incremental learning in supervised, realistic semi-supervised, and unsupervised scenarios while demonstrating continual learning in its own right.</p>