[Bayesian Learning] Model uncertainty

The models above can be used for applications as diverse as skin cancer diagnosis from lesion images, steering in autonomous vehicles, and dog breed classification in a website where users upload pictures of their pets. For example, given several pictures of dog breeds as training data—when a user uploads a photo of his dog—the hypothetical website should return a prediction with rather high confidence. But what should happen if a user uploads a photo of a cat and asks the website to decide on a dog breed?

The above is an example of out of distribution test data. The model has been trained on photos of dogs of different breeds, and has (hopefully) learnt to distinguish between them well. But the model has never seen a cat before, and a photo of a cat would lie outside of the data distribution the model was trained on. This illustrative example can be extended to more serious settings, such as MRI scans with structures a diagnostics system has never observed before, or scenes an autonomous car steering system has never been trained on.

A possible desired behaviour of a model in such cases would be to return a prediction (attempting to extrapolate far away from our observed data), but return an answer with the added information that the point lies outside of the data distribution. We want our model to possess some quantity conveying a high level of uncertainty with such inputs (alternatively, conveying low confidence).