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.

该研究介绍了一种名为 Discriminant Distance-Awareness Representation（DDAR）的新颖且高效的确定性不确定性评估方法，通过构建一个在潜在表示中包含一组原型的 DNN 模型来分析输入数据的有价值特征信息，通过松弛限制了决定性不确定性方法（DUMs）架构的利普希茨条件，从而克服了特征坍缩问题，实验结果表明 DDAR 是一种灵活且与架构无关的方法，在多个基准问题上优于现有的不确定性评估方法。