A survey of uncertainty in deep neural networks

Abstract Over the last decade, neural networks have reached almost every field of science and become a crucial part of various real world applications. Different examples from the wide spectrum of challenges in the fields of medical image analysis, robotics, and earth observation give an idea of the needs and challenges regarding uncertainties in the practical applications of neural networks.

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• 1 Due to the increasing spread, confidence in neural network predictions has become more and more important.
• 2 However, basic neural networks do not deliver certainty estimates or suffer from over- or under-confidence, i.e.
• 3 To overcome this, many researchers have been working on understanding and quantifying uncertainty in a neural network’s prediction.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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