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.
Code (0)
등록된 구현이 없습니다.
Tasks
Uncertainty QuantificationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Latent Discriminant deterministic Uncertainty
Predictive uncertainty estimation is essential for deploying Deep Neural Networks in real-world autonomous systems. However, most successful approaches are computationally intensive. In this work, we attempt to address t…
Autonomous DrivingDepth Estimationimage-classificationImage Classification+3A Simple Approach to Improve Single-Model Deep Uncertainty via Distance-Awareness
Accurate uncertainty quantification is a major challenge in deep learning, as neural networks can make overconfident errors and assign high confidence predictions to out-of-distribution (OOD) inputs. The most popular app…
Data AugmentationDeep LearningProbabilistic Deep LearningUncertainty QuantificationStochastic Vision Transformers with Wasserstein Distance-Aware Attention
Self-supervised learning is one of the most promising approaches to acquiring knowledge from limited labeled data. Despite the substantial advancements made in recent years, self-supervised models have posed a challenge …
Out-of-Distribution DetectionSelf-Supervised LearningTransfer LearningDeep Deterministic Uncertainty: A Simple Baseline
Reliable uncertainty from deterministic single-forward pass models is sought after because conventional methods of uncertainty quantification are computationally expensive. We take two complex single-forward-pass uncerta…
Active LearningUncertainty QuantificationSimple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness
Bayesian neural networks (BNN) and deep ensembles are principled approaches to estimate the predictive uncertainty of a deep learning model. However their practicality in real-time, industrial-scale applications are limi…
Uncertainty Quantification