  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.
