Software solutions
The conventional deep learning model is a supervised model. It takes months of time to develop and train the model before it is ready for the
production line. Here, Karina Odinaev, co- founder and CEO of Cortica and co- founder of artificial intelligence start-up Lean AI, explains why the conventional deep learning model is broken and what the alternatives are.
T
he conventional deep learning model is supervised. The model must be shown hundreds or thousands of pre-tagged defect images, teaching it how to determine what constitutes a defect. The process requires
significant human involvement, both from a quality manager who will have to tag the defects and the AI expert to tune the architecture and hyper parameters. This journey is not easy and can take months. The process takes thousands of images and a lot of time – typically two months for each camera and for each product type, although this can vary significantly depending on the task. You might hear bold marketing claims about the need for fewer and fewer images, but you will often find in practice that the model does not work as intended and more images and feedback are required. In many instances, the quality
manager will have to create manually by force production-like defects for training purposes. Given that these artificial defects do not necessarily represent real-world defects, it is no surprise that this approach can often lead to problems further along.
After weeks or even months of work training the model with pre-tagged data sets, the outcome is still uncertain. The system is like a black box, because when it fails you are unable to see why. Another common challenge in production is process variation. The model is required to adapt to the changes, so without this capacity for online learning you will soon encounter degradation of performance.
FULLY UNSUPERVISED MODELS The opposite of a supervised model is a fully unsupervised model. Some systems rely on part statistics to understand what is okay and what constitutes a defect. There are many challenges
SUPERVISED AND UNSUPERVISED LEARNING FOR VISUAL QUALITY INSPECTION
64 May 2023 Instrumentation Monthly
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