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 fine-grained change detection. Our research focuses on methods of harnessing the latent metric space of representation learning techniques as an interim output for hybrid human-machine intelligence. We review methods for transforming and projecting embedding space such that significant changes can be communicated more effectively and a more comprehensive interpretation of underlying relationships in sensor data is facilitated. We conduct this research in our work towards developing a method for aligning the axes of latent embedding space with meaningful real-world metrics so that the reasoning behind the detection of change in relation to past observations may be revealed and adjusted. This is an important topic in many fields concerned with producing more meaningful and explainable outputs from deep learning and also for providing means for knowledge injection and model calibration in order to maintain user confidence.
Highlights
These sensors allow the physical geometry of objects to be measured with relative immunity to illumination variations and perspective distortions, which enables simple geometric comparisons of extracted 3D shapes with simulated reference shapes to be effective for change detection [13]
The main application area for this research was industrial statistical process control (SPC), where the approach is to detect the changes in the mean of the time series, assuming the baseline process to be stationary and the shift pattern to be a step function that is sustained after the shift
The main function of Representation Learning (RL) is to encode higher-order statistics of convolutional activations/features learnt by a deep neural networks (DNNs) to enhance the mid-level learning capability, i.e., the focus is on enhancing the intermediate feature descriptor learned by a deep learning (DL) model to output a “good” representation of the input data
Summary
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Change detection (CD), the process of identifying differences in object/phenomena over time/space, is often considered a fundamental low-level preprocessing step in many data analysis problems, such as in sensor data analytics, computer vision and process trend analysis It can be considered the primary task in many real-world applications such as remote sensing, surveillance, security and healthcare. There are many applications where it is not feasible to collect data of sufficient breadth or depth for this method to be reliable, i.e., interactions between different combinations of conditions that were not accounted for at the design stage can induce variability that clouds and alters the characteristic features of significant changes, especially to each scenario For such scenarios, it is difficult for even the most modern deep learning techniques to generalise the features of changes of interest.
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