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Abstract

Introduced here is an approach to developing and then deploying machine learning models that addresses the drawbacks of conventional approaches. One objective of the approach described herein is to reduce or eliminate the need for manual labelling during the development process. To accomplish this, a surveillance system may implement self-supervised learning and knowledge distillation that rely on collaboration between its edge devices and a server system. Together, self-supervised learning and knowledge distillation ensure that the models deployed on those edge devices can be readily trained and then updated, as necessary, in order to improve inference quality without any human intervention.

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First Claim