I don't think anyone has mentioned Bayesian Neural Nets (I forget the exact term). Sure, the paradigm adds an order of magnitude overhead (at least - and that's why I've never seen it used in the industry), but you can bolt it on to existing architectures.
The basic idea is that besides the probabilities, the network also spits out confidence (IIRC based on how out-of-distribution the input is). There's been a ton of work on getting confidence values out of existing neural nets without as much overhead, but I've never seen those approaches replicate in the industry.
The basic idea is that besides the probabilities, the network also spits out confidence (IIRC based on how out-of-distribution the input is). There's been a ton of work on getting confidence values out of existing neural nets without as much overhead, but I've never seen those approaches replicate in the industry.