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Benchmarking Test-Time Unsupervised Deep Neural Network Adaptation on Edge Devices
To improve robustness of DNNs, they must be able to update themselves to enhance their prediction accuracy.
Semi-supervised on-device neural network adaptation for remote and portable laser-induced breakdown spectroscopy
However, ML for LIBS is challenging as: (i) the predictive models must be lightweight since they need to be deployed in highly resource-constrained and battery-operated portable LIBS systems; and (ii) since these systems can be remote, the models must be able to self-adapt to any domain shift in input distributions which could be due to the lack of different types of inputs in training data or dynamic environmental/sensor noise.
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