Deep Anti-Regularized Ensembles provide reliable out-of-distribution uncertainty quantification

Abstract

We consider the problem of uncertainty quantification in high dimensional regression and classification for which deep ensemble have proven to be promising methods. Recent observations have shown that deep ensemble often return overconfident estimates outside the training domain, which is a major limitation because shifted distributions are often encountered in real-life scenarios. The principal challenge for this problem is to solve the trade-off between increasing the diversity of the ensemble outputs and making accurate in-distribution predictions. In this work, we show that an ensemble of networks with large weights fitting the training data are likely to meet these two objectives. We derive a simple and practical approach to produce such ensembles, based on an original anti-regularization term penalizing small weights and a control process of the weight increase which maintains the in-distribution loss under an acceptable threshold. The developed approach does not require any out-of-distribution training data neither any trade-off hyper-parameter calibration. We derive a theoretical framework for this approach and show that the proposed optimization can be seen as a "water-filling" problem. Several experiments in both regression and classification settings highlight that Deep Anti-Regularized Ensembles (DARE) significantly improve uncertainty quantification outside the training domain in comparison to recent deep ensembles and out-of-distribution detection methods. All the conducted experiments are reproducible and the source code is available at \url{https://github.com/antoinedemathelin/DARE}.

26 pages, 9 figures

References in corpus (9)

• A Review of Uncertainty Quantification in Deep Learning: Techniques, Applications and Challenges
• A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness
• White Paper Machine Learning in Certified Systems
• Trust Issues: Uncertainty Estimation Does Not Enable Reliable OOD Detection On Medical Tabular Data
• The Peril of Popular Deep Learning Uncertainty Estimation Methods
• Improving robustness and calibration in ensembles with diversity regularization
• Agree to Disagree: Diversity through Disagreement for Better Transferability
• Semi-supervised novelty detection using ensembles with regularized disagreement
• Robust uncertainty estimates with out-of-distribution pseudo-inputs training

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