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Bayesian Neural Net
Bayesian Neural Networks: 3 Bayesian CNN
Super Deep Learning That Knows When It’s Tricked
This is the third chapter in the series on Bayesian Deep Learning. The previous article is available here.
We already know that neural networks are arrogant. But another failing of standard neural nets is a susceptibility to being tricked. Imagine a CNN tasked with a morally questionable task like face recognition. It gets its input from a security camera. But what happens when the security camera sends a picture of a dog, a mannequin or a child’s doll? The CNN will still output classifications having been tricked by something with a resemblance to human face. CNNs cry out for the Bayesian treatment, because we don’t want our work undermined by silly mistakes and because where the consequences of misclassification are high we want to know how sure the network is. How likely is a doll, or any real person, being a wanted criminal? We need to know.
Chapter Objectives
- Learn how to implement a Bayesian convolutional model
- Understand how we can identify bad input data without ever having seen it
- Understand how parameter problems of Bayesian neural networks influence training
