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

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 ... View More