arXiv Artificial Intelligence

Robust Adversarial Quantification via Conflict-Aware Evidential Deep Learning

1 hour ago 05.10.2026 07:00 👁 1 views
Robust Adversarial Quantification via Conflict-Aware Evidential Deep Learning

Quick summary

arXiv:2506.05937v3 Announce Type: replace-cross Abstract: Reliability of deep learning models is critical for deployment in high-stakes applications, where out-of-distribution or adversarial inputs may lead to detrimental outcomes. Evidential Deep Learning, an efficient paradigm for uncertainty quantification, models predictions as Dirichlet distributions of a single forward pass. However, EDL is particularly vulnerable to adversarially perturbed inputs, making overconfident errors. Conflict-aware Evidential Deep Learning~\mbox{(C-EDL)} is a lightweight post-hoc uncertainty quantification appr

Key takeaways

  • arXiv:2506.05937v3 Announce Type: replace-cross Abstract: Reliability of deep learning models is critical for deployment in high-stakes applications, where out-of-distribution or adversarial inputs may lead to detrimental outcomes.
  • Evidential Deep Learning, an efficient paradigm for uncertainty quantification, models predictions as Dirichlet distributions of a single forward pass.
  • However, EDL is particularly vulnerable to adversarially perturbed inputs, making overconfident errors.

Why it matters

“Robust Adversarial Quantification via Conflict-Aware Evidential Deep Learning” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.

Kaynak sitede devamını oku: arXiv Artificial Intelligence ↗