ETN is a lightweight post-hoc module that applies a learned sample-dependent affine transformation to pretrained model logits and interprets the outputs as Dirichlet parameters to enable efficient uncertainty estimation.
Specifically, we explain (1) how the variational distribution over A is constructed, and (2) how the prior termb is handled
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Evidential Transformation Network: Turning Pretrained Models into Evidential Models for Post-hoc Uncertainty Estimation
ETN is a lightweight post-hoc module that applies a learned sample-dependent affine transformation to pretrained model logits and interprets the outputs as Dirichlet parameters to enable efficient uncertainty estimation.