Uncertainty Estimation by Fisher Information-based Evidential Deep Learning

• URL: http://arxiv.org/abs/2303.02045v3
• Date: Fri, 30 Jun 2023 06:59:21 GMT
• Title: Uncertainty Estimation by Fisher Information-based Evidential Deep Learning
• Authors: Danruo Deng, Guangyong Chen, Yang Yu, Furui Liu, Pheng-Ann Heng
• Abstract summary: 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.
• Score: 61.94125052118442
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Uncertainty estimation is a key factor that makes deep learning reliable in practical applications. Recently proposed evidential neural networks explicitly account for different uncertainties by treating the network's outputs as evidence to parameterize the Dirichlet distribution, and achieve impressive performance in uncertainty estimation. However, for high data uncertainty samples but annotated with the one-hot label, the evidence-learning process for those mislabeled classes is over-penalized and remains hindered. To address this problem, we propose a novel method, Fisher Information-based Evidential Deep Learning ($\mathcal{I}$-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. The generalization ability of our network is further improved by optimizing the PAC-Bayesian bound. As demonstrated empirically, our proposed method consistently outperforms traditional EDL-related algorithms in multiple uncertainty estimation tasks, especially in the more challenging few-shot classification settings.

Related papers

• Uncertainty Quantification via Hölder Divergence for Multi-View Representation Learning [18.419742575630217]
  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.
  arXiv  Detail & Related papers  (2024-10-29T04:29:44Z)
• A Comprehensive Survey on Evidential Deep Learning and Its Applications [64.83473301188138]
  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.
  arXiv  Detail & Related papers  (2024-09-07T05:55:06Z)
• Pessimistic Causal Reinforcement Learning with Mediators for Confounded Offline Data [17.991833729722288]
  We propose a novel policy learning algorithm, PESsimistic CAusal Learning (PESCAL) Our key observation is that, by incorporating auxiliary variables that mediate the effect of actions on system dynamics, it is sufficient to learn a lower bound of the mediator distribution function, instead of the Q-function. We provide theoretical guarantees for the algorithms we propose, and demonstrate their efficacy through simulations, as well as real-world experiments utilizing offline datasets from a leading ride-hailing platform.
  arXiv  Detail & Related papers  (2024-03-18T14:51:19Z)
• Adaptive Negative Evidential Deep Learning for Open-set Semi-supervised Learning [69.81438976273866]
  Open-set semi-supervised learning (Open-set SSL) considers a more practical scenario, where unlabeled data and test data contain new categories (outliers) not observed in labeled data (inliers) We introduce evidential deep learning (EDL) as an outlier detector to quantify different types of uncertainty, and design different uncertainty metrics for self-training and inference. We propose a novel adaptive negative optimization strategy, making EDL more tailored to the unlabeled dataset containing both inliers and outliers.
  arXiv  Detail & Related papers  (2023-03-21T09:07:15Z)
• 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)
• Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble [16.92791301062903]
  We propose an uncertainty-based offline RL method that takes into account the confidence of the Q-value prediction and does not require any estimation or sampling of the data distribution. Surprisingly, we find that it is possible to substantially outperform existing offline RL methods on various tasks by simply increasing the number of Q-networks along with the clipped Q-learning.
  arXiv  Detail & Related papers  (2021-10-04T16:40:13Z)
• Improving the Reliability of Semantic Segmentation of Medical Images by Uncertainty Modeling with Bayesian Deep Networks and Curriculum Learning [0.0]
  We propose a novel method which leverages the uncertainty measures provided by Bayesian deep networks through curriculum learning. We show in the concrete setting of a semantic segmentation task that the proposed system is able to increase significantly the reliability of the model.
  arXiv  Detail & Related papers  (2021-08-26T10:24:02Z)
• Do Not Forget to Attend to Uncertainty while Mitigating Catastrophic Forgetting [29.196246255389664]
  One of the major limitations of deep learning models is that they face catastrophic forgetting in an incremental learning scenario. We consider a Bayesian formulation to obtain the data and model uncertainties. We also incorporate self-attention framework to address the incremental learning problem.
  arXiv  Detail & Related papers  (2021-02-03T06:54:52Z)
• Cross Learning in Deep Q-Networks [82.20059754270302]
  We propose a novel cross Q-learning algorithm, aim at alleviating the well-known overestimation problem in value-based reinforcement learning methods. Our algorithm builds on double Q-learning, by maintaining a set of parallel models and estimate the Q-value based on a randomly selected network.
  arXiv  Detail & Related papers  (2020-09-29T04:58:17Z)

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.