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Pré-Publication, Document De Travail Année : 2023

Gaussian Latent Representations for Uncertainty Estimation using Mahalanobis Distance in Deep Classifiers

Résumé

Recent works show that the data distribution in a network's latent space is useful for estimating classification uncertainty and detecting Out-Of-Distribution (OOD) samples. To obtain a well-regularized latent space that is conducive for uncertainty estimation, existing methods bring in significant changes to model architectures and training procedures. In this paper, we present a lightweight, fast, and high-performance regularization method for Mahalanobis distance (MD)-based uncertainty prediction, and that requires minimal changes to the network's architecture. To derive Gaussian latent representation favourable for MD calculation, we introduce a self-supervised representation learning method that separates in-class representations into multiple Gaussians. Classes with non-Gaussian representations are automatically identified and dynamically clustered into multiple new classes that are approximately Gaussian. Evaluation on standard OOD benchmarks shows that our method achieves state-of-the-art results on OOD detection with minimal inference time, and is very competitive on predictive probability calibration. Finally, we show the applicability of our method to a real-life computer vision use case on microorganism classification.
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Dates et versions

hal-04034465 , version 1 (17-03-2023)
hal-04034465 , version 2 (25-10-2023)

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Aishwarya Venkataramanan, Assia Benbihi, Martin Laviale, Cedric Pradalier. Gaussian Latent Representations for Uncertainty Estimation using Mahalanobis Distance in Deep Classifiers. 2023. ⟨hal-04034465v2⟩
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