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Recent works show that the data distribution in a network's latent space isuseful for estimating classification uncertainty and detectingOut-of-distribution (OOD) samples. To obtain a well-regularized latent spacethat is conducive for uncertainty estimation, existing methods bring insignificant changes to model architectures and training procedures. In thispaper, we present a lightweight, fast, and high-performance regularizationmethod for Mahalanobis distance-based uncertainty prediction, and that requiresminimal changes to the network's architecture. To derive Gaussian latentrepresentation favourable for Mahalanobis Distance calculation, we introduce aself-supervised representation learning method that separates in-classrepresentations into multiple Gaussians. Classes with non-Gaussianrepresentations are automatically identified and dynamically clustered intomultiple new classes that are approximately Gaussian. Evaluation on standardOOD benchmarks shows that our method achieves state-of-the-art results on OODdetection with minimal inference time, and is very competitive on predictiveprobability calibration. Finally, we show the applicability of our method to areal-life computer vision use case on microorganism classification.

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