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On Getting Confidence Estimates from Neural Networks | Bharath's notes

bharathpbhat.github.io · 2,928 words · saved by 1 readers

I've been reading on and off about this topic for a while now, and it comes up very often in applied ML. How confident is the neural network about a particular prediction it makes? Can we switch to a different end user experience when the network is not confident? In a typical scenario, a model is trained on a limited dataset and gets deployed in a product, where unless the user experience is strictly guarded, the model will get hit with inputs that were not anticipated. This is the problem of Out-Of-Distribution (OOD) detection - and if a model can either say that it does not "know" what to do in such cases, or equivalently, make a low confidence prediction - then that is useful for avoiding catastrophic failures. There is a lot of research that focuses on this topic, and what follows is a summary of some of the papers I've read. So how do we measure the confidence of a neural network? The majority of research focuses on Classification models, so we'll talk about them first, with a se

I've been reading on and off about this topic for a while now, and it comes up very often in applied ML. How confident is the neural network about a particular prediction it makes? Can we switch to a different end user experience when the network is not confident? In a typical scenario, a model is trained on a limited dataset and gets deployed in a product, where unless the user experience is strictly guarded, the model will get hit with inputs that were not anticipated. This is the problem of Out-Of-Distribution (OOD) detection - and if a model can either say that it does not "know" what to d

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