:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Zhou, MingWei, Pei, Xiaobing
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Artificial Intelligence
Online Access:	https://arxiv.org/abs/2504.00038
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Improving Clean Accuracy via a Tangent-Space Perspective on Adversarial Training
by: Yi, Bongsoo, et al.
Published: (2024)

Conflict-Aware Adversarial Training
by: Xue, Zhiyu, et al.
Published: (2024)

Adversarial Training: A Survey
by: Zhao, Mengnan, et al.
Published: (2024)

Enhancing Adversarial Training via Reweighting Optimization Trajectory
by: Huang, Tianjin, et al.
Published: (2023)

MemLoss: Enhancing Adversarial Training with Recycling Adversarial Examples
by: Mahdi, Soroush, et al.
Published: (2025)

Adversarial Training for Process Reward Models
by: Juneja, Gurusha, et al.
Published: (2025)

Decentralized Adversarial Training over Graphs
by: Cao, Ying, et al.
Published: (2023)

When and Why Adversarial Training Improves PINNs: A Neural Tangent Kernel Perspective
by: Cao, Yuan-dong, et al.
Published: (2026)

Better World Models Can Lead to Better Post-Training Performance
by: Gupta, Prakhar, et al.
Published: (2025)

Benign Overfitting in Adversarial Training for Vision Transformers
by: Zhang, Jiaming, et al.
Published: (2026)

DAFA: Distance-Aware Fair Adversarial Training
by: Lee, Hyungyu, et al.
Published: (2024)

Can You Break RLVER? Probing Adversarial Robustness of RL-Trained Empathetic Agents
by: K, Deeraj S, et al.
Published: (2026)

Adversarial Training for Defense Against Label Poisoning Attacks
by: Bal, Melis Ilayda, et al.
Published: (2025)

Ignition Phase : Standard Training for Fast Adversarial Robustness
by: Yu-Hang, Wang, et al.
Published: (2025)

Annealing Self-Distillation Rectification Improves Adversarial Training
by: Wu, Yu-Yu, et al.
Published: (2023)

Explanation-Guided Adversarial Training for Robust and Interpretable Models
by: Chen, Chao, et al.
Published: (2026)

Adversarial Instance Generation and Robust Training for Neural Combinatorial Optimization with Multiple Objectives
by: Liu, Wei, et al.
Published: (2026)

Adversarial Training via Adaptive Knowledge Amalgamation of an Ensemble of Teachers
by: Hamidi, Shayan Mohajer, et al.
Published: (2024)

Disttack: Graph Adversarial Attacks Toward Distributed GNN Training
by: Zhang, Yuxiang, et al.
Published: (2024)

On the Duality Between Sharpness-Aware Minimization and Adversarial Training
by: Zhang, Yihao, et al.
Published: (2024)

Information Theoretic Adversarial Training of Large Language Models
by: Zhang, Yiwei, et al.
Published: (2026)

Don't Lag, RAG: Training-Free Adversarial Detection Using RAG
by: Kazoom, Roie, et al.
Published: (2025)

TAET: Two-Stage Adversarial Equalization Training on Long-Tailed Distributions
by: YuHang, Wang, et al.
Published: (2025)

Fast Adversarial Training against Sparse Attacks Requires Loss Smoothing
by: Zhong, Xuyang, et al.
Published: (2025)

Adversarial Latent-State Training for Robust Policies in Partially Observable Domains
by: Ahuja, Angad Singh
Published: (2026)

Zero-Sacrifice Persistent-Robustness Adversarial Defense for Pre-Trained Encoders
by: Lei, Zhuxin, et al.
Published: (2026)

Robustness Feature Adapter for Efficient Adversarial Training
by: Wu, Quanwei, et al.
Published: (2025)

Structure-Guided Adversarial Training of Diffusion Models
by: Yang, Ling, et al.
Published: (2024)

AdvKT: An Adversarial Multi-Step Training Framework for Knowledge Tracing
by: Fu, Lingyue, et al.
Published: (2025)

Train Faster, Perform Better: Modular Adaptive Training in Over-Parameterized Models
by: Shi, Yubin, et al.
Published: (2024)

Embedding Hidden Adversarial Capabilities in Pre-Trained Diffusion Models
by: Beerens, Lucas, et al.
Published: (2025)

Defending Against Unforeseen Failure Modes with Latent Adversarial Training
by: Casper, Stephen, et al.
Published: (2024)

Scalable Energy-Based Models via Adversarial Training: Unifying Discrimination and Generation
by: Yin, Xuwang, et al.
Published: (2025)

Fast-Slow Co-advancing Optimizer: Toward Harmonious Adversarial Training of GAN
by: Wang, Lin, et al.
Published: (2025)

The Bid Picture: Auction-Inspired Multi-player Generative Adversarial Networks Training
by: Shim, Joo Yong, et al.
Published: (2024)

Rethinking Invariance Regularization in Adversarial Training to Improve Robustness-Accuracy Trade-off
by: Waseda, Futa, et al.
Published: (2024)

Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey
by: Schott, Lucas, et al.
Published: (2024)

Adversarial Training for Robust Coverage Network under Worst-case Facility Losses
by: Miao, Changhao, et al.
Published: (2026)

On the Clean Generalization and Robust Overfitting in Adversarial Training from Two Theoretical Views: Representation Complexity and Training Dynamics
by: Li, Binghui, et al.
Published: (2023)

Adversarially Trained Object Detector for Unsupervised Domain Adaptation
by: Fujii, Kazuma, et al.
Published: (2021)