Robust uncertainty estimates with out-of-distribution pseudo-inputs training
This addresses the robustness issue for probabilistic models in handling unexpected inputs, which is crucial for reliable AI applications, though it appears incremental as it builds on existing Bayesian frameworks.
The paper tackles the problem of poor uncertainty predictions in probabilistic models when making out-of-distribution (OOD) predictions, proposing a method to train uncertainty predictors using pseudo-inputs in low-density regions, and demonstrates robust and interpretable uncertainty predictions while maintaining state-of-the-art performance on tasks like regression and generative modeling.
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 modelling