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Lecture Notes in Computer Science
Modern deep learning systems successfully solve many perception tasks such as object pose estimation when the input image is of high quality. However, in challenging imaging conditions such as on low resolution images or when the image is corrupted by imaging artifacts, current systems degrade considerably in accuracy. While a loss in performance is unavoidable, we would like our models to quantify their uncertainty to achieve robustness against images of varying quality. Probabilistic deepdoi:10.1007/978-3-030-01240-3_33 fatcat:kqmpurcxm5fjjb6qx7wck4a4ge