A Neural Network-Based On-Device Learning Anomaly Detector for Edge Devices
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Computers"
DOI: 10.1109/tc.2020.2973631
Abstract: Semi-supervised anomaly detection is an approach to identify anomalies by learning the distribution of normal data. Backpropagation neural networks (i.e., BP-NNs) based approaches have recently drawn attention because of their good generalization capability. In a… read more here.
Keywords: edge devices; math; device learning; onlad core ... See more keywords
Enabling Weakly Supervised Temporal Action Localization From On-Device Learning of the Video Stream
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems"
DOI: 10.1109/tcad.2022.3197536
Abstract: Detecting actions in videos have been widely applied in on-device applications, such as cars, robots, etc. Practical on-device videos are always untrimmed with both action and background. It is desirable for a model to both… read more here.
Keywords: video stream; device learning; device; action ... See more keywords
Towards Full Forward On-Tiny-Device Learning: A Guided Search for a Randomly Initialized Neural Network
Sign Up to like & getrecommendations! Published in 2024 at "Algorithms"
DOI: 10.3390/a17010022
Abstract: In the context of TinyML, many research efforts have been devoted to designing forward topologies to support On-Device Learning. Reaching this target would bring numerous advantages, including reductions in latency and computational complexity, stronger privacy,… read more here.
Keywords: randomly initialized; device learning; towards full; search ... See more keywords