Traditional deep neural networks (NNs) have significantly contributed to the state-of-the-art performance in the task of classification under various application domains. However, NNs have not considered inherent uncertainty in data associated with the class probabilities where misclassification under uncertainty may easily introduce high risk in decision making in real-world contexts (e.g., misclassification of objects in roads leads to serious accidents). Unlike Bayesian NN that indirectly infer uncertainty through weight uncertainties, evidential NNs (ENNs) have been recently proposed to explicitly model the uncertainty of class probabilities and use them for classification tasks. An ENN offers the formulation of the predictions of NNs as subjective opinions and learns the function by collecting an amount of evidence that can form the subjective opinions by a deterministic NN from data. However, the ENN is trained as a black box without explicitly considering inherent uncertainty in data with their different root causes, such as vacuity (i.e., uncertainty due to a lack of evidence) or dissonance (i.e., uncertainty due to conflicting evidence). By considering the multidimensional uncertainty, we proposed a novel uncertainty-aware evidential NN called WGAN-ENN (WENN) for solving an out-of-distribution (OOD) detection problem. We took a hybrid approach that combines Wasserstein Generative Adversarial Network (WGAN) with ENNs to jointly train a model with prior knowledge of a certain class, which has high vacuity for OOD samples. Via extensive empirical experiments based on both synthetic and real-world datasets, we demonstrated that the estimation of uncertainty by WENN can significantly help distinguish OOD samples from boundary samples. WENN outperformed in OOD detection when compared with other competitive counterparts.
UT Dallas（德克萨斯大学达拉斯分校）；US Army Research Lab（美国陆军研究实验室）；Virginia Tech（弗吉尼亚理工学院暨州立大学）
Traditional deep neural nets (NNs) have shown the state-of-the-art performance in the task of classification in various applications. However, NNs have not considered any types of uncertainty associated with the class probabilities to minimize risk due to misclassification under uncertainty in real life. Unlike Bayesian neural nets indirectly infering uncertainty through weight uncertainties, evidential neural networks (ENNs) have been recently proposed to support explicit modeling of the uncertainty of class probabilities. It treats predictions of an NN as subjective opinions and learns the f…
EviDep: Uncertainty-Aware Multimodal Depression Estimation via Disentangled Evidential Learning
School of Computer Science and Technology, University of Science and Technology of China（中国科学技术大学计算机科学与技术学院）；School of Computer Science and Information Engineering, Hefei University of Technology（合肥工业大学计算机科学与信息工程学院）；Department of Psychiatry, The First Affiliated Hospital of University of Science and Technology of China, Division of Life Sciences and Medicine, University of Science and Technology of China（中国科学技术大学附属第一医院精神科，生命科学与医学学院）
In this paper, we propose TEDL, a two-stage learning approach to quantify uncertainty for deep learning models in classification tasks, inspired by our findings in experimenting with Evidential Deep Learning (EDL) method, a recently proposed uncertainty quantification approach based on the Dempster-Shafer theory. More specifically, we observe that EDL tends to yield inferior AUC compared with models learnt by cross-entropy loss and is highly sensitive in training. Such sensitivity is likely to cause unreliable uncertainty estimation, making it risky for practical applications. To mitigate both…
Equivariant Evidential Deep Learning for Interatomic Potentials
Shanghai Artificial Intelligence Laboratory（上海人工智能实验室）；College of Computer Science and Artificial Intelligence, Fudan University（复旦大学计算机科学与人工智能学院）；The Chinese University of Hong Kong（香港中文大学）；Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
IT University of Copenhagen（哥本哈根信息技术大学）；Technical University of Denmark（丹麦技术大学）；Pioneer Centre for Artificial Intelligence（先锋人工智能中心）
Popular approaches for quantifying predictive uncertainty in deep neural networks often involve distributions over weights or multiple models, for instance via Markov Chain sampling, ensembling, or Monte Carlo dropout. These techniques usually incur overhead by having to train multiple model instances or do not produce very diverse predictions. This comprehensive and extensive survey aims to familiarize the reader with an alternative class of models based on the concept of Evidential Deep Learning: For unfamiliar data, they aim to admit "what they don't know", and fall back onto a prior belief…
Southeast University（东南大学）；Nanjing University of Science and Technology（南京理工大学）；University of Shanghai for Science and Technology（上海理工大学）
An automatic vision-based sewer inspection plays a key role of sewage system in a modern city. Recent advances focus on utilizing deep learning model to realize the sewer inspection system, benefiting from the capability of data-driven feature representation. However, the inherent uncertainty of sewer defects is ignored, resulting in the missed detection of serious unknown sewer defect categories. In this paper, we propose a trustworthy multi-label sewer defect classification (TMSDC) method, which can quantify the uncertainty of sewer defect prediction via evidential deep learning. Meanwhile, …
A Comprehensive Survey on Evidential Deep Learning and Its Applications
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Peng Cheng Laboratory（鹏城实验室）
Robust Adversarial Quantification via Conflict-Aware Evidential Deep Learning
Department of Computer Science, University of York, UK（英国约克大学计算机科学系）；Department of Elect. Eng., and Computer Science and Eng., Cyprus University of Technology, Cyprus（塞浦路斯技术大学电子工程与计算机科学系）
Prediction of wind turbines power with physics-informed neural networks and evidential uncertainty quantification
University of Granada（格拉纳达大学）；Research Centre for Information and Communication Technologies (CITIC-UGR)（信息与通信技术研究中心（CITIC-UGR））；TECNALIA（泰克纳利亚研究中心）；Imperial College London（帝国理工学院）
Gryphon Center for Artificial Intelligence and Theoretical Sciences（格里芬人工智能与理论科学中心）；Georgia Institute of Technology（佐治亚理工学院）；University of Pennsylvania（宾夕法尼亚大学）
We present a novel class of Physics-Informed Neural Networks that is formulated based on the principles of Evidential Deep Learning, where the model incorporates uncertainty quantification by learning parameters of a higher-order distribution. The dependent and trainable variables of the PDE residual loss and data-fitting loss terms are recast as functions of the hyperparameters of an evidential prior distribution. Our model is equipped with an information-theoretic regularizer that contains the Kullback-Leibler divergence between two inverse-gamma distributions characterizing predictive uncer…
Mutual Evidential Deep Learning for Medical Image Segmentation
Key Laboratory of High Confidence Software Technologies (MOE) School of Computer Science Peking University（高可信软件技术重点实验室（MOE）计算机科学学院，北京大学）；College of Computer and Information Science School of Software Southwest University（计算机与信息科学学院 软件学院 西南大学）；Department of Computer and Information Science University of Macau（计算机与信息科学系 澳门大学）
FADEL: Uncertainty-aware Fake Audio Detection with Evidential Deep Learning
Department of Electrical and Computer Engineering and INMC, Seoul National University, Seoul, South Korea（电子与计算机工程系和INMC，首尔国立大学）；Department of AI, Chung-Ang University, Seoul, South Korea（人工智能系， Chung-Ang 大学）
Université de technologie de Compiègne（贡比涅技术大学）；CNRS（法国国家科学研究中心）；Institut universitaire de France（法兰西大学研究院）；Shanghai University（上海大学）
Evidential clustering is an approach to clustering based on the use of Dempster-Shafer mass functions to represent cluster-membership uncertainty. In this paper, we introduce a neural-network based evidential clustering algorithm, called NN-EVCLUS, which learns a mapping from attribute vectors to mass functions, in such a way that more similar inputs are mapped to output mass functions with a lower degree of conflict. The neural network can be paired with a one-class support vector machine to make it robust to outliers and allow for novelty detection. The network is trained to minimize the dis…
Department of Chemical Engineering, University of Michigan（美国密歇根大学化学工程系）；Michigan Institute for Data & AI in Society, University of Michigan（美国密歇根大学数据与社会人工智能研究所）；Department of Statistics, University of Michigan（美国密歇根大学统计系）
The Conditional Neural Process (CNP) family of models offer a promising direction to tackle few-shot problems by achieving better scalability and competitive predictive performance. However, the current CNP models only capture the overall uncertainty for the prediction made on a target data point. They lack a systematic fine-grained quantification on the distinct sources of uncertainty that are essential for model training and decision-making under the few-shot setting. We propose Evidential Conditional Neural Processes (ECNP), which replace the standard Gaussian distribution used by CNP with …
Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications
National Center for Atmospheric Research（国家大气研究中心）；Cooperative Institute for Severe and High-Impact Weather Research and Operations（恶劣与高影响天气研究与合作研究所）；National Severe Storms Laboratory（国家强风暴实验室）；University Corporation for Atmospheric Research（大学大气研究公司）；University of Maryland（马里兰大学）；University at Albany, State University of New York（纽约州立大学奥尔巴尼分校）；Colorado State University（科罗拉多州立大学）；The Pennsylvania State University（宾夕法尼亚州立大学）
University of Texas at Dallas（德克萨斯大学达拉斯分校）；NEC Laboratories America（NEC美国实验室）
Sound Event Early Detection (SEED) is an essential task in recognizing the acoustic environments and soundscapes. However, most of the existing methods focus on the offline sound event detection, which suffers from the over-confidence issue of early-stage event detection and usually yield unreliable results. To solve the problem, we propose a novel Polyphonic Evidential Neural Network (PENet) to model the evidential uncertainty of the class probability with Beta distribution. Specifically, we use a Beta distribution to model the distribution of class probabilities, and the evidential uncertain…
Uncertainty-Supervised Interpretable and Robust Evidential Segmentation
School of Data Science, Fudan University, Shanghai, China（复旦大学数据科学学院）；Institute of Science and Technology for Brain-inspired intelligence, Fudan University, Shanghai, China（复旦大学脑启发式智能技术研究所）；Nuffield Department of Population Health, University of Oxford, Oxford, UK（牛津大学人口健康牛津部门）
Evidential Deep Active Learning for Semi-Supervised Classification
College of Information Engineering, Northwest A&F University（信息工程学院，西北农林科技大学）；Hangzhou Dianzi University（杭州电子科技大学）；Department of Communications Sciences, University of Teramo（通讯科学系，乌尔巴诺-特拉莫大学）
Imperial College London（帝国理工学院）；TECNALIA（TECNALIA（西班牙技术中心））；University of the Basque Country (UPV/EHU)（巴斯克大学）；Royal Brompton Hospital（皇家布朗普顿医院）
Despite recent advances in the accuracy of brain tumor segmentation, the results still suffer from low reliability and robustness. Uncertainty estimation is an efficient solution to this problem, as it provides a measure of confidence in the segmentation results. The current uncertainty estimation methods based on quantile regression, Bayesian neural network, ensemble, and Monte Carlo dropout are limited by their high computational cost and inconsistency. In order to overcome these challenges, Evidential Deep Learning (EDL) was developed in recent work but primarily for natural image classific…
Hyper Evidential Deep Learning to Quantify Composite Classification Uncertainty
The University of Texas at Dallas（德克萨斯大学达拉斯分校）；US Army Research Laboratory（美国陆军研究实验室）；University of Oslo（奥斯陆大学）；Virginia Tech（弗吉尼亚理工大学）；University of the District of Columbia（哥伦比亚特区大学）
Heidelberg University（海德堡大学）；Bosch Center for Artificial Intelligence（博世人工智能中心）
We propose a novel method for closed-form predictive distribution modeling with neural nets. In quantifying prediction uncertainty, we build on Evidential Deep Learning, which has been impactful as being both simple to implement and giving closed-form access to predictive uncertainty. We employ it to model aleatoric uncertainty and extend it to account also for epistemic uncertainty by converting it to a Bayesian Neural Net. While extending its uncertainty quantification capabilities, we maintain its analytically accessible predictive distribution model by performing progressive moment matchin…
Uncertainty-Error correlations in Evidential Deep Learning models for biomedical segmentation
Gryphon Center for Artificial Intelligence and Theoretical Sciences, Singapore University of Pennsylvania, Perelman School of Medicine, Department of Radiation Oncology（格雷芬人工智能与理论科学中心，新加坡大学匹兹堡大学，佩尔曼医学学院，放射肿瘤学系）；Georgia Institute of Technology（佐治亚理工学院）；University of Pennsylvania, Perelman School of Medicine, Department of Radiation Oncology（宾夕法尼亚大学，佩尔曼医学学院，放射肿瘤学系）
Toward Efficient Uncertainty in LLMs through Evidential Knowledge Distillation
Department of Computer Science and Engineering, Indian Institute of Technology Hyderabad（计算机科学与工程系，印度理工学院海得拉巴分校）；Jagiellonian University, Faculty of Mathematics and Computer Science（雅盖隆大学，数学与计算机科学学院）
DPsurv: Dual-Prototype Evidential Fusion for Uncertainty-Aware and Interpretable Whole-Slide Image Survival Prediction
National University of Singapore；National University of Singapore Guangzhou Research Translation；Innovation Institute；Imperial College London；Peking Union Medical College Hospital, Chinese Academy of Medical Sciences \& Peking Union Medical College；Hunan University；Institute of High Performance Computing, Agency for Science, Technology；Research (A STAR)
EvidentialRAG: Quantifying and Mitigating Information Conflict in Multi-Source Retrieval-Augmented Generation via Evidential Deep Learning
School of Computing, Wichita State University（威奇托州立大学计算学院）；Department of Computer Science, American International University-Bangladesh（美国国际孟加拉大学计算机科学系）
