A third option is to use Bayesian neural networks (Blundell et al., 2015;Louizos and Welling, 2017), which treat model parameters as probability distributions to represent model uncertainty explicitly.
these Bayesian approaches are often prohibitive, as the priors of the model parameters are difficult to specify, and exact inference is intractable, which can also lead to unreliable uncertainty estimates.