Image de couverture de livre

Bridging Symmetry and Robustness: On the Role of Equivariance in Enhancing Adversarial Robustness

Fuente: arXiv

Enregistré dans:

Détails bibliographiques
Auteurs principaux:	Wang, Longwei, Uddin, Ifrat Ikhtear, Santosh, KC, Zhang, Chaowei, Qin, Xiao, Zhou, Yang
Format:	Preprint
Publié:	2025
Sujets:	Machine Learning Artificial Intelligence
Accès en ligne:	Acceder al recurso
Tags:	Ajouter un tag Pas de tags, Soyez le premier à ajouter un tag!

Documents similaires

Expert-Guided Explainable Few-Shot Learning for Medical Image Diagnosis
par: Uddin, Ifrat Ikhtear, et autres
Publié: (2025)

Expert-Guided Explainable Few-Shot Learning with Active Sample Selection for Medical Image Analysis
par: Wang, Longwei, et autres
Publié: (2026)

Learning to Select Like Humans: Explainable Active Learning for Medical Imaging
par: Uddin, Ifrat Ikhtear, et autres
Publié: (2026)

Toward Carbon-Neutral Human AI: Rethinking Data, Computation, and Learning Paradigms for Sustainable Intelligence
par: Santosh, KC, et autres
Publié: (2025)

Enhancing Adversarial Robustness of Deep Neural Networks Through Supervised Contrastive Learning
par: Wang, Longwei, et autres
Publié: (2024)

Bridging Interpretability and Robustness Using LIME-Guided Model Refinement
par: Nayyem, Navid, et autres
Publié: (2024)

Winsor-CAM: Human-Tunable Visual Explanations from Deep Networks via Layer-Wise Winsorization
par: Wall, Casey, et autres
Publié: (2025)

Explainability-Guided Defense: Attribution-Aware Model Refinement Against Adversarial Data Attacks
par: Wang, Longwei, et autres
Publié: (2026)

Symmetry in the Wild: The Role of Equivariance in Neural Fluid Surrogates
par: Rygiel, Patryk, et autres
Publié: (2026)

Ecologically Valid Benchmarking and Adaptive Attention: Scalable Marine Bioacoustic Monitoring
par: Rasmussen, Nicholas R., et autres
Publié: (2025)

AI-CARE: Carbon-Aware Reporting Evaluation Metric for AI Models
par: Santosh, KC, et autres
Publié: (2026)

Adversarial Examples Might be Avoidable: The Role of Data Concentration in Adversarial Robustness
par: Pal, Ambar, et autres
Publié: (2023)

Enhancing Adversarial Robustness with Conformal Prediction: A Framework for Guaranteed Model Reliability
par: Bao, Jie, et autres
Publié: (2025)

Adversarial Preference Learning for Robust LLM Alignment
par: Wang, Yuanfu, et autres
Publié: (2025)

Equivariant Symmetry Breaking Sets
par: Xie, YuQing, et autres
Publié: (2024)

Exploring Adversarial Robustness of Deep State Space Models
par: Qi, Biqing, et autres
Publié: (2024)

Zero-Sacrifice Persistent-Robustness Adversarial Defense for Pre-Trained Encoders
par: Lei, Zhuxin, et autres
Publié: (2026)

Maintaining Adversarial Robustness in Continuous Learning
par: Ru, Xiaolei, et autres
Publié: (2024)

Adversarially Robust Decision Transformer
par: Tang, Xiaohang, et autres
Publié: (2024)

Towards Reliable Evaluation of Adversarial Robustness for Spiking Neural Networks
par: Wang, Jihang, et autres
Publié: (2025)

Are Robust LLM Fingerprints Adversarially Robust?
par: Nasery, Anshul, et autres
Publié: (2025)

Bridging Models to Defend: A Population-Based Strategy for Robust Adversarial Defense
par: Wang, Ren, et autres
Publié: (2023)

Adversarial Diffusion for Robust Reinforcement Learning
par: Foffano, Daniele, et autres
Publié: (2025)

Algorithms for Adversarially Robust Deep Learning
par: Robey, Alexander
Publié: (2025)

Robust Decision Aggregation with Adversarial Experts
par: Guo, Yongkang, et autres
Publié: (2024)

Adversarial Robustness Overestimation and Instability in TRADES
par: Li, Jonathan Weiping, et autres
Publié: (2024)

Exploring the Impact of Temperature Scaling in Softmax for Classification and Adversarial Robustness
par: Xuan, Hao, et autres
Publié: (2025)

Ignition Phase : Standard Training for Fast Adversarial Robustness
par: Yu-Hang, Wang, et autres
Publié: (2025)

UACER: An Uncertainty-Adaptive Critic Ensemble Framework for Robust Adversarial Reinforcement Learning
par: Wu, Jiaxi, et autres
Publié: (2025)

SHIELD: A Secure and Highly Enhanced Integrated Learning for Robust Deepfake Detection against Adversarial Attacks
par: Uddin, Kutub, et autres
Publié: (2025)

Dynamic Adversarial Reinforcement Learning for Robust Multimodal Large Language Models
par: Bao, Yicheng, et autres
Publié: (2026)

Robustness-enhanced Uplift Modeling with Adversarial Feature Desensitization
par: Sun, Zexu, et autres
Publié: (2023)

VCAT: Vulnerability-aware and Curiosity-driven Adversarial Training for Enhancing Autonomous Vehicle Robustness
par: Cai, Xuan, et autres
Publié: (2024)

Can Large Language Models Improve the Adversarial Robustness of Graph Neural Networks?
par: Zhang, Zhongjian, et autres
Publié: (2024)

Sy-FAR: Symmetry-based Fair Adversarial Robustness
par: Najjar, Haneen, et autres
Publié: (2025)

How Worst-Case Are Adversarial Attacks? Linking Adversarial and Perturbation Robustness
par: Rossolini, Giulio
Publié: (2026)

Towards Adversarially Robust Deep Metric Learning
par: Ke, Xiaopeng
Publié: (2025)

CEAR: Certified Ensemble Adversarial Robustness in DNNs
par: Sadig, Daniel, et autres
Publié: (2026)

Adversarial Robustness of VAEs across Intersectional Subgroups
par: Ramanaik, Chethan Krishnamurthy, et autres
Publié: (2024)

ROKA: Robust Knowledge Unlearning against Adversaries
par: Shin, Jinmyeong, et autres
Publié: (2026)