arXiv Computer Vision
CalCErt: Bin-wise Certification of Confidence Calibration in Medical Image Classification
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arXiv AI
Jul 1
Improving Certified Robustness via Adversarial Distillation
arXiv:2606. 31653v1 Announce Type: cross Abstract: Certified training aims to produce models whose predictions can be formally verified against adversarial perturbations, typically by optimising upper bounds on the worst-case loss over an allowed perturbation set.
arXiv AI
5d ago
Robust Adversarial Quantification via Conflict-Aware Evidential Deep Learning
The paper introduces Conflict‑Aware Evidential Deep Learning (C‑EDL), a lightweight post‑hoc method that improves uncertainty quantification for deep learning models. C‑EDL applies diverse, task‑preserving transformations to each input and uses representational disagreement to adjust predictions, thereby reducing overconfident errors on adversarial and out‑of‑distribution data. Experiments demonstrate that C‑EDL outperforms existing Evidential Deep Learning variants and baselines, achieving up to 55 % reduction in coverage for OOD data and 90 % for adversarial data across multiple datasets and attack types.
arXiv AI
Jun 2
Calibrating Uncertainty for Zero-Shot Adversarial CLIP
arXiv:2512. 12997v2 Announce Type: replace-cross Abstract: CLIP delivers strong zero-shot classification but remains highly vulnerable to adversarial attacks.
arXiv Machine Learning
Aug 12
Invertible Logits Transformation for Accuracy-Preserving Post-Hoc Uncertainty Calibration
arXiv:2608. 10372v1 Announce Type: new Abstract: Post-hoc calibration aligns a classifier's predicted confidences with its empirical accuracy without retraining.
arXiv AI
Jun 2
CEAR: Certified Ensemble Adversarial Robustness in DNNs
arXiv:2606. 01437v1 Announce Type: cross Abstract: Deep Neural Networks (DNNs) are highly susceptible to adversarial perturbations, leading to extensive research on robustness for safety-critical applications.
arXiv Machine Learning
Jul 7
Robustness Meets Uncertainty: Evidential Adversarial Training for Robust Selective Classification
arXiv:2607. 03075v1 Announce Type: new Abstract: Safety-critical applications require classifiers that are both robust and reliable.