Human Supervised Learning for Visual Quality Inspection

Once a model is deployed in production, it requires careful maintenance. During production, unforeseen issues can arise, and humans are essential for immediate problem-solving and recalibration of the model. The synergy of machine learning models with human expertise is therefore crucial to optimize the production quality control process. Human feedback is vital for refining the model’s understanding of evolving production conditions. As product variations or new quality issues emerge, human input guides the model’s adaptation and validation, ensuring it remains relevant and effective in real-time quality control. 

In this use case active learning is being utilized, where humans bring domain knowledge and expertise to label the most relevant data points to the model. This is particularly important as it allows effective allocation of resources, to devote them to labelling data that is most likely to enhance model accuracy.