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Discriminant Distance-Aware Representation on Deterministic Uncertainty Quantification Methods

Jiaxin Zhang, Kamalika Das, Sricharan Kumar

TL;DR

This work tackles reliable uncertainty estimation in safety-critical systems by introducing Discriminant Distance-Aware Representation (DDAR), a deterministic method that constructs a distance-aware latent space using learnable prototypes and a Distinction Maximization layer. DDAR forgoes strict Lipschitz regularization in favor of a discriminative framework that preserves sample-specific information and remains architecture-agnostic, enabling a single forward-pass uncertainty estimate. The proposed loss includes a RBF-based uncertainty term plus two regularizers to prevent prototype collapse and encourage diverse prototype usage, yielding improved OOD detection and competitive calibration across vision and NLP tasks. Empirically, DDAR demonstrates superior or competitive performance on the Two Moons toy task, CIFAR-10/100 vs SVHN, and CLINC out-of-scope detection, suggesting practical benefits for deploying scalable, efficient uncertainty estimation in real-world systems.

Abstract

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.

Discriminant Distance-Aware Representation on Deterministic Uncertainty Quantification Methods

TL;DR

This work tackles reliable uncertainty estimation in safety-critical systems by introducing Discriminant Distance-Aware Representation (DDAR), a deterministic method that constructs a distance-aware latent space using learnable prototypes and a Distinction Maximization layer. DDAR forgoes strict Lipschitz regularization in favor of a discriminative framework that preserves sample-specific information and remains architecture-agnostic, enabling a single forward-pass uncertainty estimate. The proposed loss includes a RBF-based uncertainty term plus two regularizers to prevent prototype collapse and encourage diverse prototype usage, yielding improved OOD detection and competitive calibration across vision and NLP tasks. Empirically, DDAR demonstrates superior or competitive performance on the Two Moons toy task, CIFAR-10/100 vs SVHN, and CLINC out-of-scope detection, suggesting practical benefits for deploying scalable, efficient uncertainty estimation in real-world systems.

Abstract

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.
Paper Structure (16 sections, 12 equations, 3 figures, 5 tables, 1 algorithm)

This paper contains 16 sections, 12 equations, 3 figures, 5 tables, 1 algorithm.

Table of Contents

  1. Introduction
  2. Background
  3. Prototype Learning.
  4. Deterministic Uncertainty Estimation.
  5. Methodology
  6. Feature Collapse Issue
  7. Distance-aware Latent Representation
  8. Discriminant Loss with Regularization Constraints
  9. Experiments
  10. Toy Example: Two Moons
  11. OOD Detection: CIFAR-10/100 vs SVHN
  12. Conversational Language Understanding
  13. Related Work
  14. Deterministic Uncertainty Methods.
  15. Addressing Feature Collapse.
  16. ...and 1 more sections

Figures (3)

  • Figure 1: DDAR overview: an efficient, distance-aware and architecture-agnostic method for deterministic uncertainty estimation. DDAR learns a discriminative distance-aware latent representation by leveraging the learnable prototypes and distinction maximization layer. Combined with the RBF kernel, our method performs accurate uncertainty estimation and competitive OOD detection capabilities.
  • Figure 2: Uncertainty results of different baseline methods on the two moon 2D classification benchmarks. Yellow indicates uncertainty, while dark blue indicates certainty. The first row is the decision boundary (class probability) and the second row is the predictive uncertainty.
  • Figure 3: Addressing feature collapse. 2D project of feature embedding of regular DNN (left), DDAR embedding (middle), and DDAR embedding after the DM layer, trained on the two moons dataset.