A Comprehensive Survey on Evidential Deep Learning and Its Applications

• URL: http://arxiv.org/abs/2409.04720v1
• Date: Sat, 7 Sep 2024 05:55:06 GMT
• Title: A Comprehensive Survey on Evidential Deep Learning and Its Applications
• Authors: Junyu Gao, Mengyuan Chen, Liangyu Xiang, Changsheng Xu,
• Abstract summary: Evidential Deep Learning (EDL) provides reliable uncertainty estimation with minimal additional computation in a single forward pass. We first delve into the theoretical foundation of EDL, the subjective logic theory, and discuss its distinctions from other uncertainty estimation frameworks. We elaborate on its extensive applications across various machine learning paradigms and downstream tasks.
• Score: 64.83473301188138
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Reliable uncertainty estimation has become a crucial requirement for the industrial deployment of deep learning algorithms, particularly in high-risk applications such as autonomous driving and medical diagnosis. However, mainstream uncertainty estimation methods, based on deep ensembling or Bayesian neural networks, generally impose substantial computational overhead. To address this challenge, a novel paradigm called Evidential Deep Learning (EDL) has emerged, providing reliable uncertainty estimation with minimal additional computation in a single forward pass. This survey provides a comprehensive overview of the current research on EDL, designed to offer readers a broad introduction to the field without assuming prior knowledge. Specifically, we first delve into the theoretical foundation of EDL, the subjective logic theory, and discuss its distinctions from other uncertainty estimation frameworks. We further present existing theoretical advancements in EDL from four perspectives: reformulating the evidence collection process, improving uncertainty estimation via OOD samples, delving into various training strategies, and evidential regression networks. Thereafter, we elaborate on its extensive applications across various machine learning paradigms and downstream tasks. In the end, an outlook on future directions for better performances and broader adoption of EDL is provided, highlighting potential research avenues.

Related papers

• A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation [0.0]
  Advancements in image segmentation play an integral role within the greater scope of Deep Learning-based computer vision. Uncertainty quantification has been extensively studied within this context, enabling expression of model ignorance (epistemic uncertainty) or data ambiguity (aleatoric uncertainty) to prevent uninformed decision making. This work provides a comprehensive overview of probabilistic segmentation by discussing fundamental concepts in uncertainty that govern advancements in the field and the application to various tasks.
  arXiv  Detail & Related papers  (2024-11-25T13:26:09Z)
• Rethinking the Uncertainty: A Critical Review and Analysis in the Era of Large Language Models [42.563558441750224]
  Large Language Models (LLMs) have become fundamental to a broad spectrum of artificial intelligence applications. Current methods often struggle to accurately identify, measure, and address the true uncertainty. This paper introduces a comprehensive framework specifically designed to identify and understand the types and sources of uncertainty.
  arXiv  Detail & Related papers  (2024-10-26T15:07:15Z)
• A Survey of Uncertainty Estimation in LLMs: Theory Meets Practice [7.687545159131024]
  We clarify the definitions of uncertainty and confidence, highlighting their distinctions and implications for model predictions. We categorize various classes of uncertainty estimation methods derived from approaches. We also explore techniques for uncertainty into diverse applications, including out-of-distribution detection, data annotation, and question clarification.
  arXiv  Detail & Related papers  (2024-10-20T07:55:44Z)
• Chain-of-Thought Prompting for Demographic Inference with Large Multimodal Models [58.58594658683919]
  Large multimodal models (LMMs) have shown transformative potential across various research tasks. Our findings indicate LMMs possess advantages in zero-shot learning, interpretability, and handling uncurated 'in-the-wild' inputs. We propose a Chain-of-Thought augmented prompting approach, which effectively mitigates the off-target prediction issue.
  arXiv  Detail & Related papers  (2024-05-24T16:26:56Z)
• Are Uncertainty Quantification Capabilities of Evidential Deep Learning a Mirage? [35.15844215216846]
  EDL methods are trained to learn a meta distribution over the predictive distribution by minimizing a specific objective function. Recent studies identify limitations of the existing methods to conclude their learned uncertainties are unreliable. We provide a sharper understanding of the behavior of a wide class of EDL methods by unifying various objective functions. We conclude that even when EDL methods are empirically effective on downstream tasks, this occurs despite their poor uncertainty quantification capabilities.
  arXiv  Detail & Related papers  (2024-02-09T03:23:39Z)
• Uncertainty Estimation by Fisher Information-based Evidential Deep Learning [61.94125052118442]
  Uncertainty estimation is a key factor that makes deep learning reliable in practical applications. We propose a novel method, Fisher Information-based Evidential Deep Learning ($mathcalI$-EDL) In particular, we introduce Fisher Information Matrix (FIM) to measure the informativeness of evidence carried by each sample, according to which we can dynamically reweight the objective loss terms to make the network more focused on the representation learning of uncertain classes.
  arXiv  Detail & Related papers  (2023-03-03T16:12:59Z)
• Offline Reinforcement Learning with Instrumental Variables in Confounded Markov Decision Processes [93.61202366677526]
  We study the offline reinforcement learning (RL) in the face of unmeasured confounders. We propose various policy learning methods with the finite-sample suboptimality guarantee of finding the optimal in-class policy.
  arXiv  Detail & Related papers  (2022-09-18T22:03:55Z)
• The Unreasonable Effectiveness of Deep Evidential Regression [72.30888739450343]
  A new approach with uncertainty-aware regression-based neural networks (NNs) shows promise over traditional deterministic methods and typical Bayesian NNs. We detail the theoretical shortcomings and analyze the performance on synthetic and real-world data sets, showing that Deep Evidential Regression is a quantification rather than an exact uncertainty.
  arXiv  Detail & Related papers  (2022-05-20T10:10:32Z)
• On the uncertainty of self-supervised monocular depth estimation [52.13311094743952]
  Self-supervised paradigms for monocular depth estimation are very appealing since they do not require ground truth annotations at all. We explore for the first time how to estimate the uncertainty for this task and how this affects depth accuracy. We propose a novel peculiar technique specifically designed for self-supervised approaches.
  arXiv  Detail & Related papers  (2020-05-13T09:00:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.