# Estimating Predictive Uncertainty for unlabeled data

I am trying to estimate the predictive uncertainty for a deep neural network. While I do have a labeled training set, I´m trying to measure uncertainty for some unlabeled production data.

This paper proposes the use of Deep Ensembles and Adversarial Training to compute a measurement of uncertainty. However, it uses Brier Score as a metric which requires me to know the real label of my production data. Is there a similar way or metric which does not require labeled data?

Another approach was described by Yarin Gal utilizing Monte Carlo Dropout. However I can´t get any useful results using that technique on my model.

I want to use my model in an online learning task. As the given data may very over time, I need to detect examples where my model is highly uncertain, so I can manually classify those examples.

inputs = keras.Input(shape=(10,))

By setting training=True it remains active in during test time. This can be called Monte Carlo dropout or more popularly dropout at inference time. I hope it helps.