Discriminant Distance-Aware Representation on Deterministic Uncertainty Quantification Methods
<|reference_start|>Discriminant Distance-Aware Representation on Deterministic Uncertainty Quantification Methods: 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.<|reference_end|>
@article{zhang2024discriminant,
    title={Discriminant Distance-Aware Representation on Deterministic Uncertainty
  Quantification Methods},
    author={Jiaxin Zhang, Kamalika Das, Sricharan Kumar},
    journal={arXiv preprint arXiv:2402.12664},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.12664},
    primaryClass={cs.LG}
}