Uncertainty quantification for computer vision
As a domain, computer vision has transformed many sectors by automating complex tasks that were once reserved for human eyes and cognition. Computer vision models have become an integral part of modern technology, whether it’s detecting pedestrians on the road, identifying potential tumours in medical scans, or even analyzing satellite images for environmental studies. However, as the reliance on these models grows, so does the need to understand and quantify the uncertainty associated with their predictions.
Why does uncertainty matter?
Before deep-diving into the mechanics, it’s essential to understand why we need uncertainty quantification (UQ) in the first place. Let’s go through some of the reasons as follows:
- Safety and reliability: A wrong prediction can have severe consequences in critical applications, such as medical imaging or autonomous driving. Knowing the confidence level in a prediction...
