Improving Out-of-Distribution Detection via Epistemic Uncertainty Adversarial Training. (arXiv:2209.03148v1 [cs.LG])

The quantification of uncertainty is important for the adoption of machine
learning, especially to reject out-of-distribution (OOD) data back to human
experts for review. Yet progress has been slow, as a balance must be struck
between computational efficiency and the quality of uncertainty estimates. For
this reason many use deep ensembles of neural networks or Monte Carlo dropout
for reasonable uncertainty estimates at relatively minimal compute and memory.
Surprisingly, when we focus on the real-world applicable constraint of $\leq
1\%$ …

arxiv detection distribution training uncertainty