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Active Foundational Models for Fault Diagnosis of Electrical Motors
To overcome this limitation, we propose a foundational model-based Active Learning framework that utilizes less amount of labeled samples, which are most informative and harnesses a large amount of available unlabeled data by effectively combining Active Learning and Contrastive Self-Supervised Learning techniques.
Towards AI enabled automated tracking of multiple boxers
However, continuous tracking of multiple athletes across multiple training sessions remains a challenge, because it is difficult to precisely segment bout boundaries in a recorded video stream.
Foundational Models for Fault Diagnosis of Electrical Motors
However, the data distribution can vary across different operating conditions during real-world operating scenarios of electrical motors.
Addressing practical challenges in Active Learning via a hybrid query strategy
Inspired by these practical challenges, we present a hybrid query strategy-based AL framework that addresses three practical challenges simultaneously: cold-start, oracle uncertainty and performance evaluation of Active Learner in the absence of ground truth.
