Sparse modelling with small datasets

With the recent rise in interest in artificial intelligence for computer vision applications, a lot of attention has been given to the potential benefits that AI can bring – promises of more accurate quality control inspection with fewer false alarms and lower cost. However, when deployed, these goals often aren't met – in fact, 85 per cent of AI projects fail. Some of the reasons behind these numbers are that most of the efforts to deploy AI to date assume the availability of large amounts of data to train the model, ignore the importance of being able to explain how the algorithm arrived at its conclusion, and lack consideration for compute resource requirements. Often, in today's complex manufacturing processes, there aren't enough examples of defects to create an accurate model. Furthermore, and perhaps more importantly, commonly used methods such as deep learning are a black box, only able to provide results but not show the method used to reach a conclusion.