Training, Architecture, and Prior for Deterministic Uncertainty Methods

• URL: http://arxiv.org/abs/2303.05796v2
• Date: Tue, 28 Mar 2023 18:34:38 GMT
• Title: Training, Architecture, and Prior for Deterministic Uncertainty Methods
• Authors: Bertrand Charpentier, Chenxiang Zhang, Stephan G\"unnemann
• Abstract summary: This work investigates important design choices in Deterministic Uncertainty Methods (DUMs) We show that training schemes decoupling the core architecture and the uncertainty head schemes can significantly improve uncertainty performances. Contrary to other Bayesian models, we show that the prior defined by DUMs do not have a strong effect on the final performances.
• Score: 33.45069308137142
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate and efficient uncertainty estimation is crucial to build reliable Machine Learning (ML) models capable to provide calibrated uncertainty estimates, generalize and detect Out-Of-Distribution (OOD) datasets. To this end, Deterministic Uncertainty Methods (DUMs) is a promising model family capable to perform uncertainty estimation in a single forward pass. This work investigates important design choices in DUMs: (1) we show that training schemes decoupling the core architecture and the uncertainty head schemes can significantly improve uncertainty performances. (2) we demonstrate that the core architecture expressiveness is crucial for uncertainty performance and that additional architecture constraints to avoid feature collapse can deteriorate the trade-off between OOD generalization and detection. (3) Contrary to other Bayesian models, we show that the prior defined by DUMs do not have a strong effect on the final performances.

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