Amir Salimi Lafmejani

Imagine trying to train a self-driving car to navigate safely through a city without enough examples of rare but critical scenarios like pedestrians jaywalking or unexpected road hazards. Or think about developing a medical imaging system that must detect anomalies that occur in only 0.01% of cases. In both situations, data imbalance becomes a major challenge—where common scenarios are overrepresented while rare, yet crucial, events are scarce. Collecting enough real-world data is not just difficult; it’s often expensive, time-consuming, and riddled with privacy concerns. This is where vision data simulation platforms come into play.