Knowledge from Uncertainty in Evidential Deep Learning
Cai Davies Marc Roig Vilamala Alun D. Preece Federico Cerutti Lance M. Kaplan Supriyo Chakraborty
This work reveals an evidential signal that emerges from the uncertainty
value in Evidential Deep Learning (EDL). EDL is one example of a class of
uncertainty-aware deep learning approaches designed to provide confidence (or
epistemic uncertainty) about the current test sample. In particular for
computer vision and bidirectional encoder large language models, the
`evidential signal' arising from the Dirichlet strength in EDL can, in some
cases, discriminate between classes, which is particularly strong when using
large language models. We hypothesise that the KL regularisation term causes
EDL to couple aleatoric and epistemic uncertainty. In this paper, we
empirically investigate the correlations between misclassification and
evaluated uncertainty, and show that EDL's `evidential signal' is due to
misclassification bias. We critically evaluate EDL with other Dirichlet-based
approaches, namely Generative Evidential Neural Networks (EDL-GEN) and Prior
Networks, and show theoretically and empirically the differences between these
loss functions. We conclude that EDL's coupling of uncertainty arises from
these differences due to the use (or lack) of out-of-distribution samples
during training.