Abstract:Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many recent technologies that leverage big data and deep learning struggle with this task. This review focuses on the state-of-the-art methods, applications, and challenges of representation learning for… Show more
“…A review by O'Mahony et al [23] focuses on the state-of-the-art methods, applications, and challenges of representation learning for fine-grained change detection. Fine-grained change detection in sensor data is very challenging for artificial intelligence, though it is critically important in practice.…”
Section: Sensor Network and Smart/intelligent Sensorsmentioning
“…A review by O'Mahony et al [23] focuses on the state-of-the-art methods, applications, and challenges of representation learning for fine-grained change detection. Fine-grained change detection in sensor data is very challenging for artificial intelligence, though it is critically important in practice.…”
Section: Sensor Network and Smart/intelligent Sensorsmentioning
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