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Improving a machine’s ability to reason about the unknown has been a prominent commonality across the different emerging areas of modern supervised learning. While there are different approaches that formalize this problem, many focus on generalized target recognition tailored to the known vs unknown problem setting. Overall, these approaches have created a meaningful foundation that promotes algorithm enhancement with respect to factors like detection, robustness, and internal knowledge expression. However, one major shortcoming across numerous prior works is the question of how to make use of unknown classifications for an algorithm deployment setting. Herein, we address this shortcoming by proposing an self-supervised comparison assessment methodology for computer vision tasks. Specifically, we leverage the features of foundational models across different dimensionality spaces to facilitate a comparison analysis of unknown information. Preliminary results are encouraging and demonstrate that this process not only has benefits in computer vision applications, but also is flexible for methodology alterations.
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