Uncertainty Quantification via Hölder Divergence for Multi-View Representation Learning

• URL: http://arxiv.org/abs/2411.00826v1
• Date: Tue, 29 Oct 2024 04:29:44 GMT
• Title: Uncertainty Quantification via Hölder Divergence for Multi-View Representation Learning
• Authors: an Zhang, Ming Li, Chun Li, Zhaoxia Liu, Ye Zhang, Fei Richard Yu,
• Abstract summary: This paper introduces a novel algorithm based on H"older Divergence (HD) to enhance the reliability of multi-view learning. Through the Dempster-Shafer theory, integration of uncertainty from different modalities, thereby generating a comprehensive result. Mathematically, HD proves to better measure the distance'' between real data distribution and predictive distribution of the model.
• Score: 18.419742575630217
• License:
• Abstract: Evidence-based deep learning represents a burgeoning paradigm for uncertainty estimation, offering reliable predictions with negligible extra computational overheads. Existing methods usually adopt Kullback-Leibler divergence to estimate the uncertainty of network predictions, ignoring domain gaps among various modalities. To tackle this issue, this paper introduces a novel algorithm based on H\"older Divergence (HD) to enhance the reliability of multi-view learning by addressing inherent uncertainty challenges from incomplete or noisy data. Generally, our method extracts the representations of multiple modalities through parallel network branches, and then employs HD to estimate the prediction uncertainties. Through the Dempster-Shafer theory, integration of uncertainty from different modalities, thereby generating a comprehensive result that considers all available representations. Mathematically, HD proves to better measure the ``distance'' between real data distribution and predictive distribution of the model and improve the performances of multi-class recognition tasks. Specifically, our method surpass the existing state-of-the-art counterparts on all evaluating benchmarks. We further conduct extensive experiments on different backbones to verify our superior robustness. It is demonstrated that our method successfully pushes the corresponding performance boundaries. Finally, we perform experiments on more challenging scenarios, \textit{i.e.}, learning with incomplete or noisy data, revealing that our method exhibits a high tolerance to such corrupted data.

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)
• One step closer to unbiased aleatoric uncertainty estimation [71.55174353766289]
  We propose a new estimation method by actively de-noising the observed data. By conducting a broad range of experiments, we demonstrate that our proposed approach provides a much closer approximation to the actual data uncertainty than the standard method.
  arXiv  Detail & Related papers  (2023-12-16T14:59:11Z)
• Implicit Variational Inference for High-Dimensional Posteriors [7.924706533725115]
  In variational inference, the benefits of Bayesian models rely on accurately capturing the true posterior distribution. We propose using neural samplers that specify implicit distributions, which are well-suited for approximating complex multimodal and correlated posteriors. Our approach introduces novel bounds for approximate inference using implicit distributions by locally linearising the neural sampler.
  arXiv  Detail & Related papers  (2023-10-10T14:06:56Z)
• Quantification of Predictive Uncertainty via Inference-Time Sampling [57.749601811982096]
  We propose a post-hoc sampling strategy for estimating predictive uncertainty accounting for data ambiguity. The method can generate different plausible outputs for a given input and does not assume parametric forms of predictive distributions.
  arXiv  Detail & Related papers  (2023-08-03T12:43:21Z)
• Towards General Visual-Linguistic Face Forgery Detection [95.73987327101143]
  Deepfakes are realistic face manipulations that can pose serious threats to security, privacy, and trust. Existing methods mostly treat this task as binary classification, which uses digital labels or mask signals to train the detection model. We propose a novel paradigm named Visual-Linguistic Face Forgery Detection(VLFFD), which uses fine-grained sentence-level prompts as the annotation.
  arXiv  Detail & Related papers  (2023-07-31T10:22:33Z)
• 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)
• Uncertain Facial Expression Recognition via Multi-task Assisted Correction [43.02119884581332]
  We propose a novel method of multi-task assisted correction in addressing uncertain facial expression recognition called MTAC. Specifically, a confidence estimation block and a weighted regularization module are applied to highlight solid samples and suppress uncertain samples in every batch. Experiments on RAF-DB, AffectNet, and AffWild2 datasets demonstrate that the MTAC obtains substantial improvements over baselines when facing synthetic and real uncertainties.
  arXiv  Detail & Related papers  (2022-12-14T10:28:08Z)
• Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picture [5.868139834982011]
  Uncertainty estimation is essential to make neural networks trustworthy in real-world applications. We propose a new multi-view classification framework for better uncertainty estimation and out-of-domain sample detection.
  arXiv  Detail & Related papers  (2022-10-06T04:47:51Z)
• NUQ: Nonparametric Uncertainty Quantification for Deterministic Neural Networks [151.03112356092575]
  We show the principled way to measure the uncertainty of predictions for a classifier based on Nadaraya-Watson's nonparametric estimate of the conditional label distribution. We demonstrate the strong performance of the method in uncertainty estimation tasks on a variety of real-world image datasets.
  arXiv  Detail & Related papers  (2022-02-07T12:30:45Z)
• Improving Regression Uncertainty Estimation Under Statistical Change [7.734726150561088]
  We propose and implement a loss function for regression uncertainty estimation based on the Bayesian Validation Metric framework. A series of experiments on in-distribution data show that the proposed method is competitive with existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-09-16T20:32:58Z)
• Learning while Respecting Privacy and Robustness to Distributional Uncertainties and Adversarial Data [66.78671826743884]
  The distributionally robust optimization framework is considered for training a parametric model. The objective is to endow the trained model with robustness against adversarially manipulated input data. Proposed algorithms offer robustness with little overhead.
  arXiv  Detail & Related papers  (2020-07-07T18:25:25Z)

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.