Class Level Confidence

Class-level confidence, in machine learning, refers to the model's estimated certainty about its predictions for each individual class, rather than an overall prediction confidence. Current research focuses on improving the accuracy and reliability of these class-specific confidence scores, particularly addressing challenges like imbalanced datasets and noisy labels. This is achieved through techniques such as dynamic thresholding, class-specific calibration methods, and confidence-based sieving strategies, improving model robustness and performance in various applications, including object detection, semi-supervised learning, and domain adaptation. The improved calibration and reliability of class-level confidence scores are crucial for building more trustworthy and reliable machine learning systems across diverse domains.