Robust uncertainty estimates with out-of-distribution pseudo-inputs training

Pierre Segonne, Yevgen Zainchkovskyy, Søren Hauberg

Abstract:

Probabilistic models often use neural networks to control their predictive uncertainty.
However, when making out-of-distribution (OOD) predictions, the often uncontrollable
extrapolation properties of neural networks yield poor uncertainty predictions.
Such models then don’t know what they don’t know, which directly limits
their robustness w.r.t unexpected inputs. To counter this, we propose to explicitly train
the uncertainty predictor where we are not given data to make it reliable.
As one cannot train without data, we provide mechanisms for generating pseudo-inputs
in informative low-density regions of the input space, and show how to leverage these
in a practical Bayesian framework that casts a prior distribution over the model uncertainty.
With a holistic evaluation, we demonstrate that this yields robust and interpretable predictions
of uncertainty while retaining state-of-the-art performance on diverse tasks such as regression
and generative modeling.

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Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/robust-uncertainty-estimates-with-out-of/code)