Imagen de Portada

Defense without Forgetting: Continual Adversarial Defense with Anisotropic & Isotropic Pseudo Replay

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Zhou, Yuhang, Hua, Zhongyun
Formato:	Preprint
Publicado:	2024
Materias:	Machine Learning Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

CORE: Mitigating Catastrophic Forgetting in Continual Learning through Cognitive Replay
por: Zhang, Jianshu, et al.
Publicado: (2024)

Regret-Based Defense in Adversarial Reinforcement Learning
por: Belaire, Roman, et al.
Publicado: (2023)

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

Mitigating the Structural Bias in Graph Adversarial Defenses
por: Fang, Junyuan, et al.
Publicado: (2025)

MaskPure: Improving Defense Against Text Adversaries with Stochastic Purification
por: Gietz, Harrison, et al.
Publicado: (2024)

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

Dual-LoRA and Quality-Enhanced Pseudo Replay for Multimodal Continual Food Learning
por: Wu, Xinlan, et al.
Publicado: (2025)

On the Implicit Adversariality of Catastrophic Forgetting in Deep Continual Learning
por: Peng, Ze, et al.
Publicado: (2025)

FOREVER: Forgetting Curve-Inspired Memory Replay for Language Model Continual Learning
por: Feng, Yujie, et al.
Publicado: (2026)

TFGN: Task-Free, Replay-Free Continual Pre-Training Without Catastrophic Forgetting at LLM Scale
por: Ganguli, Anurup
Publicado: (2026)

Deep Adversarial Defense Against Multilevel-Lp Attacks
por: Wang, Ren, et al.
Publicado: (2024)

Contrastive ECOC: Learning Output Codes for Adversarial Defense
por: Chou, Che-Yu, et al.
Publicado: (2025)

Rethinking Adversarial Policies: A Generalized Attack Formulation and Provable Defense in RL
por: Liu, Xiangyu, et al.
Publicado: (2023)

Routing without Forgetting
por: Masano, Alessio, et al.
Publicado: (2026)

Graph Transductive Defense: a Two-Stage Defense for Graph Membership Inference Attacks
por: Niu, Peizhi, et al.
Publicado: (2024)

Towards Robust Policy: Enhancing Offline Reinforcement Learning with Adversarial Attacks and Defenses
por: Nguyen, Thanh, et al.
Publicado: (2024)

Reasoning as an Adaptive Defense for Safety
por: Kim, Taeyoun, et al.
Publicado: (2025)

GeRe: Towards Efficient Anti-Forgetting in Continual Learning of LLM via General Samples Replay
por: Zhang, Yunan, et al.
Publicado: (2025)

AdaMixup: A Dynamic Defense Framework for Membership Inference Attack Mitigation
por: Chen, Ying, et al.
Publicado: (2025)

Adversarial Robustness in Financial Machine Learning: Defenses, Economic Impact, and Governance Evidence
por: Baviskar, Samruddhi
Publicado: (2025)

Robust and Resource-Efficient Data-Free Knowledge Distillation by Generative Pseudo Replay
por: Binici, Kuluhan, et al.
Publicado: (2022)

MirrorCheck: Efficient Adversarial Defense for Vision-Language Models
por: Fares, Samar, et al.
Publicado: (2024)

IoT-based Android Malware Detection Using Graph Neural Network With Adversarial Defense
por: Yumlembam, Rahul, et al.
Publicado: (2025)

Enhancing Security in Deep Reinforcement Learning: A Comprehensive Survey on Adversarial Attacks and Defenses
por: Yichao, Wu, et al.
Publicado: (2025)

Forget Forgetting: Continual Learning in a World of Abundant Memory
por: Cho, Dongkyu, et al.
Publicado: (2025)

Bridging Models to Defend: A Population-Based Strategy for Robust Adversarial Defense
por: Wang, Ren, et al.
Publicado: (2023)

Better Generative Replay for Continual Federated Learning
por: Qi, Daiqing, et al.
Publicado: (2023)

Adversarial Text Purification: A Large Language Model Approach for Defense
por: Moraffah, Raha, et al.
Publicado: (2024)

Privacy-preserving Universal Adversarial Defense for Black-box Models
por: Li, Qiao, et al.
Publicado: (2024)

In-Training Defenses against Emergent Misalignment in Language Models
por: Kaczér, David, et al.
Publicado: (2025)

Alert-ME: An Explainability-Driven Defense Against Adversarial Examples in Transformer-Based Text Classification
por: Sabir, Bushra, et al.
Publicado: (2023)

Adversarial Reinforcement Learning for Offensive and Defensive Agents in a Simulated Zero-Sum Network Environment
por: Shahid, Abrar, et al.
Publicado: (2025)

One Step to the Side: Why Defenses Against Malicious Finetuning Fail Under Adaptive Adversaries
por: Zloczower, Itay, et al.
Publicado: (2026)

May the Forgetting Be with You: Alternate Replay for Learning with Noisy Labels
por: Millunzi, Monica, et al.
Publicado: (2024)

Catastrophic Forgetting Mitigation via Discrepancy-Weighted Experience Replay
por: Xu, Xinrun, et al.
Publicado: (2025)

Unveiling and Addressing Pseudo Forgetting in Large Language Models
por: Sun, Huashan, et al.
Publicado: (2024)

Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach
por: Mohan, Adithya, et al.
Publicado: (2025)

Memory Efficient Full-gradient Attacks (MEFA) Framework for Adversarial Defense Evaluations
por: Du, Yuan, et al.
Publicado: (2026)

CoMemNet: Contrastive Sampling with Memory Replay Network for Continual Traffic Prediction
por: Wu, Mei, et al.
Publicado: (2026)

Adversarial Vulnerability Transcends Computational Paradigms: Feature Engineering Provides No Defense Against Neural Adversarial Transfer
por: Hsain, Achraf, et al.
Publicado: (2026)