Reduction of Class Activation Uncertainty with Background Information
The method introduces a background class during model training to reduce uncertainty in class activation maps. By training with this extra class, the model learns to ignore irrelevant background patterns when scoring target classes, leading to better generalization than standard multitask learning while requiring less computational effort.