Over-MAP: Structural Attention Mechanism and Automated Semantic Segmentation Ensembled for Uncertainty Prediction

Both theoretical and practical problems in deep learning classification require solutions for assessing uncertainty prediction but current state-of-the-art methods this area are computationally expensive. In paper, we propose a new confidence measure dubbed Over-MAP that utilizes of overlap between structural attention mechanisms segmentation methods, is particular interest accurate fine-grained contexts. We show increases with the degree overlap. The associated identification tools conceptually simple, efficient, high as they allow weeding out misleading examples training data. Our currently deployed real-world on widely used platforms to annotate large-scale data efficiently.