AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
This study presents the first comprehensive evaluation framework systematically assessing XAI robustness under natural image corruptions encountered in production environments and establishes the first evidence-based XAI robustness ranking under natural corruptions, providing actionable guidance for practitioners selecting methods in real-world applications where input quality cannot be guaranteed.
This review explores the key techniques for explainability in deep visual recognition, including model-agnostic methods such as LIME and SHAP, model-specific approaches like saliency maps and feature visualization, and intrinsically interpretable models like decision trees and rule-based systems.
This work identifies a progressive decay of robust features across network layers and establishes a functional dependency between the prevalence of these features and model performance, and proposes Suppress and Diversify (S\&D), a non-intrusive refinement approach that enhances robustness by dynamically selecting robu...
This paper proposes BRUCE (Benchmarking Robustness Under Corruption Escalation), a multimodal reasoning fragility framework for scientific vision-language reasoning that evaluates VLMs'robustness by applying perturbations and distortions to the input images, such as blur or low contrast.
It is suggested that predictive performance, attribution plausibility, and mechanistic faithfulness characterize different aspects of model behavior and should be evaluated separately when studying explainability in media bias detection.
Feature-robust Augmentation is introduced, which comprises diversified degradation-aware augmentation strategies, and a supervised contrastive learning pattern paired with a mean-teacher architecture that stabilizes features against augmentations through consistency constraints that wins the first place in ACM Multimed...
It is concluded that explainability is a necessary, though not sufficient, condition for trustworthy Al, and concrete directions for future research are outlined, including standardised benchmarks, human-centred evaluation, and explainability for large generative models.
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