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Streamlined and Resource-Efficient Predictive Uncertainty Estimation of Deep Ensemble Predictions via Regression
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  • Jordan F. Masakuna ,
  • D'Jeff K. Nkashama ,
  • Arian Soltani ,
  • Marc Frappier ,
  • Pierre-Martin Tardif ,
  • Froduald Kabanza
Jordan F. Masakuna
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D'Jeff K. Nkashama
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Arian Soltani
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Marc Frappier
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Pierre-Martin Tardif
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Froduald Kabanza
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This paper highlights the contribution of utilizing ensemble deep learning with auto-encoders (AEs) for out-of-distribution data detection. The key innovation is treating ensemble UQ as a regression problem, mapping uncertainty distribution to a single model, reducing computational demands. This approach aligns well with the ensemble of AEs’ uncertainty distribution, making it valuable for resource-constrained systems and rapid decision-making in computational intelligence.