  Uncertainty estimation is a crucial aspect of deploying dependable deep
learning models in safety-critical systems. In this study, we introduce a novel
and efficient method for deterministic uncertainty estimation called
Discriminant Distance-Awareness Representation (DDAR). Our approach involves
constructing a DNN model that incorporates a set of prototypes in its latent
representations, enabling us to analyze valuable feature information from the
input data. By leveraging a distinction maximization layer over optimal
trainable prototypes, DDAR can learn a discriminant distance-awareness
representation. We demonstrate that DDAR overcomes feature collapse by relaxing
the Lipschitz constraint that hinders the practicality of deterministic
uncertainty methods (DUMs) architectures. Our experiments show that DDAR is a
flexible and architecture-agnostic method that can be easily integrated as a
pluggable layer with distance-sensitive metrics, outperforming state-of-the-art
uncertainty estimation methods on multiple benchmark problems.
