Investigating Feasibility of Active Learning with Image Content on Mobile Devices Using ELM
Proceedings of ELM2019Proceedings in Adaptation, Learning and Optimization(2020)
Abstract
This work investigates the feasibility of using the computational resources of a mobile device in active learning usage scenarios. It addresses the two main concerns, namely a way of fast model training or updating when more labels become available without re-training the whole Deep Learning model in case of image analysis, and the feasibility of running active learning workloads directly on a mobile device to improve the responsiveness and avoid using Cloud computing resources that become expensive at a large scale.
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Key words
Extreme Learning Machine, iOS, Edge computing, Active learning
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