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Uncertainty Estimation For Deep Neural Networks

Ali Harakeh, Steven L. Waslander

One of the challenging aspects of incorporating deep neural networks into robotic systems is the lack of uncertainty measures associated with their output predictions. Recent work has identified aleatoric and epistemic as two types of uncertainty in the output of deep neural networks, and provided methods for their estimation. This project aims to resolve challenges involved in estimating these two forms of uncertainty for a verity of perception tasks involved in robotics, including but not limited to object detection.

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