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2021 IEEE Winter Conference on Applications of Computer Vision (WACV)
In this paper, we propose risk-calibrated evidential deep classifiers to reduce the costs associated with classification errors. We use two main approaches. The first is to develop methods to quantify the uncertainty of a classifier's predictions and reduce the likelihood of acting on erroneous predictions. The second is a novel way to train the classifier such that erroneous classifications are biased towards less risky categories. We combine these two approaches in a principled way. Whiledoi:10.1109/wacv48630.2021.00253 fatcat:uzi2qsdlsjhi5k6jyreet5vi64