Making the most of quite little: Improving AI training for edge sensor time series

Overview of the proposed data augmentation approach. Credit: Tokyo Institute of Technology

Engineers at the Tokyo Institute of Technology (Tokyo Tech) have demonstrated a simple computational approach for improving the way artificial intelligence classifiers, such as neural networks, can be trained based on limited amounts of sensor data. The emerging applications of the Internet of Things often require edge devices that can reliably classify behaviors and situations based on time series.

However, training data are difficult and expensive to acquire. The proposed approach promises to substantially increase the quality of classifier training, at almost no extra cost.
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