TRAINING OF DEPLOYED NEURAL NETWORKS

TRAINING OF DEPLOYED NEURAL NETWORKS

Embodiments presented herein facilitate improvement of a deployed neural network model's accuracy without significantly affecting its operation. In one or more embodiments, online training of the deployed model may be performed using a second neural network model that has higher accuracy than t...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Hauptverfasser: KOU, Haofeng, ZHENG, Huimeng
Format: Patent
Sprache:eng
Schlagworte:
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Beschreibung
Zusammenfassung:Embodiments presented herein facilitate improvement of a deployed neural network model's accuracy without significantly affecting its operation. In one or more embodiments, online training of the deployed model may be performed using a second neural network model that has higher accuracy than the deployed neural network model. In one or more embodiments, the second neural network model may also be improved online. Embodiments may be deployed in system, such as edge computing environments, in which neural networks deployed at the edge can be centrally monitored and updated.