Uncertainty-Informed Volume Visualization using Implicit Neural Representation

• URL: http://arxiv.org/abs/2408.06018v1
• Date: Mon, 12 Aug 2024 09:14:23 GMT
• Title: Uncertainty-Informed Volume Visualization using Implicit Neural Representation
• Authors: Shanu Saklani, Chitwan Goel, Shrey Bansal, Zhe Wang, Soumya Dutta, Tushar M. Athawale, David Pugmire, Christopher R. Johnson,
• Abstract summary: We propose uncertainty-aware implicit neural representations to model scalar field data sets. We evaluate the effectiveness of two principled deep uncertainty estimation techniques. Our work makes it suitable for robustly analyzing and visualizing real-world scientific volumetric data sets.
• Score: 6.909370175721755
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The increasing adoption of Deep Neural Networks (DNNs) has led to their application in many challenging scientific visualization tasks. While advanced DNNs offer impressive generalization capabilities, understanding factors such as model prediction quality, robustness, and uncertainty is crucial. These insights can enable domain scientists to make informed decisions about their data. However, DNNs inherently lack ability to estimate prediction uncertainty, necessitating new research to construct robust uncertainty-aware visualization techniques tailored for various visualization tasks. In this work, we propose uncertainty-aware implicit neural representations to model scalar field data sets effectively and comprehensively study the efficacy and benefits of estimated uncertainty information for volume visualization tasks. We evaluate the effectiveness of two principled deep uncertainty estimation techniques: (1) Deep Ensemble and (2) Monte Carlo Dropout (MCDropout). These techniques enable uncertainty-informed volume visualization in scalar field data sets. Our extensive exploration across multiple data sets demonstrates that uncertainty-aware models produce informative volume visualization results. Moreover, integrating prediction uncertainty enhances the trustworthiness of our DNN model, making it suitable for robustly analyzing and visualizing real-world scientific volumetric data sets.

Related papers

• Uncertainty-Aware Deep Neural Representations for Visual Analysis of Vector Field Data [12.557846998225104]
  We develop uncertainty-aware implicit neural representations to model steady-state vector fields effectively. Our detailed exploration using several vector data sets indicate that uncertainty-aware models generate informative visualization results of vector field features.
  arXiv  Detail & Related papers  (2024-07-23T01:59:58Z)
• Visual Analysis of Prediction Uncertainty in Neural Networks for Deep Image Synthesis [3.09988520562118]
  It is imperative to comprehend the quality, confidence, robustness, and uncertainty associated with their prediction. A thorough understanding of these quantities produces actionable insights that help application scientists make informed decisions. This contribution demonstrates how the prediction uncertainty and sensitivity of DNNs can be estimated efficiently using various methods.
  arXiv  Detail & Related papers  (2024-05-22T20:01:31Z)
• Uncertainty in Graph Neural Networks: A Survey [50.63474656037679]
  Graph Neural Networks (GNNs) have been extensively used in various real-world applications. However, the predictive uncertainty of GNNs stemming from diverse sources can lead to unstable and erroneous predictions. This survey aims to provide a comprehensive overview of the GNNs from the perspective of uncertainty.
  arXiv  Detail & Related papers  (2024-03-11T21:54:52Z)
• What Matters When Repurposing Diffusion Models for General Dense Perception Tasks? [49.84679952948808]
  Recent works show promising results by simply fine-tuning T2I diffusion models for dense perception tasks. We conduct a thorough investigation into critical factors that affect transfer efficiency and performance when using diffusion priors. Our work culminates in the development of GenPercept, an effective deterministic one-step fine-tuning paradigm tailed for dense visual perception tasks.
  arXiv  Detail & Related papers  (2024-03-10T04:23:24Z)
• Uncertainty-aware deep learning for digital twin-driven monitoring: Application to fault detection in power lines [0.0]
  Deep neural networks (DNNs) are often coupled with physics-based models or data-driven surrogate models to perform fault detection and health monitoring of systems in the low data regime. These models can exhibit parametric uncertainty that propagates to the generated data. In this article, we quantify the impact of both these sources of uncertainty on the performance of the DNN.
  arXiv  Detail & Related papers  (2023-03-20T09:27:58Z)
• Interpretable Self-Aware Neural Networks for Robust Trajectory Prediction [50.79827516897913]
  We introduce an interpretable paradigm for trajectory prediction that distributes the uncertainty among semantic concepts. We validate our approach on real-world autonomous driving data, demonstrating superior performance over state-of-the-art baselines.
  arXiv  Detail & Related papers  (2022-11-16T06:28:20Z)
• Exploring the Trade-off between Plausibility, Change Intensity and Adversarial Power in Counterfactual Explanations using Multi-objective Optimization [73.89239820192894]
  We argue that automated counterfactual generation should regard several aspects of the produced adversarial instances. We present a novel framework for the generation of counterfactual examples.
  arXiv  Detail & Related papers  (2022-05-20T15:02:53Z)
• Confidence Aware Neural Networks for Skin Cancer Detection [12.300911283520719]
  We present three different methods for quantifying uncertainties for skin cancer detection from images. The obtained results reveal that the predictive uncertainty estimation methods are capable of flagging risky and erroneous predictions. We also demonstrate that ensemble approaches are more reliable in capturing uncertainties through inference.
  arXiv  Detail & Related papers  (2021-07-19T19:21:57Z)
• Accurate and Robust Feature Importance Estimation under Distribution Shifts [49.58991359544005]
  PRoFILE is a novel feature importance estimation method. We show significant improvements over state-of-the-art approaches, both in terms of fidelity and robustness.
  arXiv  Detail & Related papers  (2020-09-30T05:29:01Z)
• Deep Transparent Prediction through Latent Representation Analysis [0.0]
  The paper presents a novel deep learning approach, which extracts latent information from trained Deep Neural Networks (DNNs) and derives concise representations that are analyzed in an effective, unified way for prediction purposes. Transparency combined with high prediction accuracy are the targeted goals of the proposed approach.
  arXiv  Detail & Related papers  (2020-09-13T19:21:40Z)
• Diversity inducing Information Bottleneck in Model Ensembles [73.80615604822435]
  In this paper, we target the problem of generating effective ensembles of neural networks by encouraging diversity in prediction. We explicitly optimize a diversity inducing adversarial loss for learning latent variables and thereby obtain diversity in the output predictions necessary for modeling multi-modal data. Compared to the most competitive baselines, we show significant improvements in classification accuracy, under a shift in the data distribution.
  arXiv  Detail & Related papers  (2020-03-10T03:10:41Z)

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.