تقرير
Discriminant Distance-Aware Representation on Deterministic Uncertainty Quantification Methods
| العنوان: | Discriminant Distance-Aware Representation on Deterministic Uncertainty Quantification Methods |
|---|---|
| المؤلفون: | Zhang, Jiaxin, Das, Kamalika, Kumar, Sricharan |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Machine Learning |
| الوصف: | 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. Comment: AISTATS 2024 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2402.12664 |
| رقم الأكسشن: | edsarx.2402.12664 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |