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.

提出了一种基于 Fisher Information-based 的 Evidential Deep Learning 方法，该方法通过测量样本携带的信息量动态地重新加权目标损失项，使网络更专注于不确定类别的表示学习，在多个数据不确定性评估任务中具有更好的性能表现，特别是在更具挑战性的 few-shot 分类环境下。

基于 Fisher 信息的证据深度学习的不确定性估计

Uncertainty Estimation by Fisher Information-based Evidential Deep Learning

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.

本文提出了一种新型的基于 WGAN 的 evidential neural network (WENN)，通过在模型训练中进行多维不确定性建模，增强了 OOD 检测的性能，从而在绝大多数数据集上优于对手模型。

多维不确定性感知证据神经网络

Multidimensional Uncertainty-Aware Evidential Neural Networks

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 function by collecting the evidence leading to these opinions by a
deterministic NN from data. However, an ENN is trained as a black box without
explicitly considering different types of inherent data uncertainty, such as
vacuity (uncertainty due to a lack of evidence) or dissonance (uncertainty due
to conflicting evidence). This paper presents a new approach, called a {\em
regularized ENN}, that learns an ENN based on regularizations related to
different characteristics of inherent data uncertainty. Via the experiments
with both synthetic and real-world datasets, we demonstrate that the proposed
regularized ENN can better learn of an ENN modeling different types of
uncertainty in the class probabilities for classification tasks.

本文介绍了一种称为 “规则化 ENN” 的新方法，旨在基于不同特性相关的正则化学习 ENN，以更好地对类别概率中的不确定性进行建模，通过对合成和真实数据集的实验，证明了所提出的规则化 ENN 可以更好地学习模拟不同类型的数据不确定性的 ENN。