Ginsberg-Rising

Ginsberg-Rising is a concept from object detection models, particularly in the context of training neural networks. It describes a phenomenon where, as training progresses, the model’s predictions gradually become more confident and accurate, causing the "rise" of true positive signals. Essentially, it's the process where the model's ability to correctly identify objects improves over time, leading to stronger detection scores. This term emphasizes the dynamic improvement in detection performance during training, reflecting the model's learning curve and increasing reliability in recognizing relevant features in data.