Computer Science > Computer Vision and Pattern Recognition
[Submitted on 4 Sep 2023]
Title:On the Query Strategies for Efficient Online Active Distillation
View PDFAbstract:Deep Learning (DL) requires lots of time and data, resulting in high computational demands. Recently, researchers employ Active Learning (AL) and online distillation to enhance training efficiency and real-time model adaptation. This paper evaluates a set of query strategies to achieve the best training results. It focuses on Human Pose Estimation (HPE) applications, assessing the impact of selected frames during training using two approaches: a classical offline method and a online evaluation through a continual learning approach employing knowledge distillation, on a popular state-of-the-art HPE dataset. The paper demonstrates the possibility of enabling training at the edge lightweight models, adapting them effectively to new contexts in real-time.
Submission history
From: Enrico Martini Mr [view email][v1] Mon, 4 Sep 2023 13:53:20 UTC (4,832 KB)
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