Towards Adversarially Robust Dataset Distillation by Curvature Regularization
Fuente:
arXiv
Saved in:
| Main Authors: | Xue, Eric, Li, Yijiang, Liu, Haoyang, Wang, Peiran, Shen, Yifan, Wang, Haohan |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Dataset Distillation via the Wasserstein Metric
by: Liu, Haoyang, et al.
Published: (2023)
by: Liu, Haoyang, et al.
Published: (2023)
Towards Understanding Adversarial Transferability in Federated Learning
by: Li, Yijiang, et al.
Published: (2023)
by: Li, Yijiang, et al.
Published: (2023)
Approximate Nullspace Augmented Finetuning for Robust Vision Transformers
by: Liu, Haoyang, et al.
Published: (2024)
by: Liu, Haoyang, et al.
Published: (2024)
Foundation Model-oriented Robustness: Robust Image Model Evaluation with Pretrained Models
by: Zhang, Peiyan, et al.
Published: (2023)
by: Zhang, Peiyan, et al.
Published: (2023)
Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models
by: Zhou, Andy, et al.
Published: (2023)
by: Zhou, Andy, et al.
Published: (2023)
Choosing Wisely and Learning Deeply: Selective Cross-Modality Distillation via CLIP for Domain Generalization
by: Leng, Jixuan, et al.
Published: (2023)
by: Leng, Jixuan, et al.
Published: (2023)
Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks
by: Qiao, Yu, et al.
Published: (2024)
by: Qiao, Yu, et al.
Published: (2024)
IMPROVE: Iterative Model Pipeline Refinement and Optimization Leveraging LLM Experts
by: Xue, Eric, et al.
Published: (2025)
by: Xue, Eric, et al.
Published: (2025)
Towards Trustworthy Dataset Distillation
by: Ma, Shijie, et al.
Published: (2023)
by: Ma, Shijie, et al.
Published: (2023)
RegMix: Adversarial Mutual and Generalization Regularization for Enhancing DNN Robustness
by: Liu, Zhenyu, et al.
Published: (2025)
by: Liu, Zhenyu, et al.
Published: (2025)
Mitigating Accuracy-Robustness Trade-off via Balanced Multi-Teacher Adversarial Distillation
by: Zhao, Shiji, et al.
Published: (2023)
by: Zhao, Shiji, et al.
Published: (2023)
Improving Adversarial Robustness of Attribution via Implicit Regularization
by: Mehrpanah, Amir, et al.
Published: (2026)
by: Mehrpanah, Amir, et al.
Published: (2026)
Understanding Dataset Distillation via Spectral Filtering
by: Bo, Deyu, et al.
Published: (2025)
by: Bo, Deyu, et al.
Published: (2025)
Improving Adversarial Robust Fairness via Anti-Bias Soft Label Distillation
by: Zhao, Shiji, et al.
Published: (2023)
by: Zhao, Shiji, et al.
Published: (2023)
Revisiting Semi-supervised Adversarial Robustness via Noise-aware Online Robust Distillation
by: Wu, Tsung-Han, et al.
Published: (2024)
by: Wu, Tsung-Han, et al.
Published: (2024)
Towards Robust Content Watermarking Against Removal and Forgery Attacks
by: Zhu, Yifan, et al.
Published: (2026)
by: Zhu, Yifan, et al.
Published: (2026)
Dataset Distillers Are Good Label Denoisers In the Wild
by: Cheng, Lechao, et al.
Published: (2024)
by: Cheng, Lechao, et al.
Published: (2024)
Pooling Image Datasets With Multiple Covariate Shift and Imbalance
by: Chytas, Sotirios Panagiotis, et al.
Published: (2024)
by: Chytas, Sotirios Panagiotis, et al.
Published: (2024)
Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks
by: Zhou, Andy, et al.
Published: (2024)
by: Zhou, Andy, et al.
Published: (2024)
BadLabel: A Robust Perspective on Evaluating and Enhancing Label-noise Learning
by: Zhang, Jingfeng, et al.
Published: (2023)
by: Zhang, Jingfeng, et al.
Published: (2023)
DD-RobustBench: An Adversarial Robustness Benchmark for Dataset Distillation
by: Wu, Yifan, et al.
Published: (2024)
by: Wu, Yifan, et al.
Published: (2024)
Group Distributionally Robust Dataset Distillation with Risk Minimization
by: Vahidian, Saeed, et al.
Published: (2024)
by: Vahidian, Saeed, et al.
Published: (2024)
CPFD: Confidence-aware Privileged Feature Distillation for Short Video Classification
by: Shi, Jinghao, et al.
Published: (2024)
by: Shi, Jinghao, et al.
Published: (2024)
Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training
by: Zhi, Hongxin, et al.
Published: (2025)
by: Zhi, Hongxin, et al.
Published: (2025)
Temporal Saliency-Guided Distillation: A Scalable Framework for Distilling Video Datasets
by: Gu, Xulin, et al.
Published: (2025)
by: Gu, Xulin, et al.
Published: (2025)
Adversarial Score identity Distillation: Rapidly Surpassing the Teacher in One Step
by: Zhou, Mingyuan, et al.
Published: (2024)
by: Zhou, Mingyuan, et al.
Published: (2024)
Enhancing Adversarial Robustness via Uncertainty-Aware Distributional Adversarial Training
by: Dong, Junhao, et al.
Published: (2024)
by: Dong, Junhao, et al.
Published: (2024)
RAID: A Dataset for Testing the Adversarial Robustness of AI-Generated Image Detectors
by: Eddoubi, Hicham, et al.
Published: (2025)
by: Eddoubi, Hicham, et al.
Published: (2025)
Large Scale Diffusion Distillation via Score-Regularized Continuous-Time Consistency
by: Zheng, Kaiwen, et al.
Published: (2025)
by: Zheng, Kaiwen, et al.
Published: (2025)
Improved Diffusion-based Generative Model with Better Adversarial Robustness
by: Wang, Zekun, et al.
Published: (2025)
by: Wang, Zekun, et al.
Published: (2025)
Exploring the Impact of Dataset Bias on Dataset Distillation
by: Lu, Yao, et al.
Published: (2024)
by: Lu, Yao, et al.
Published: (2024)
ERDE: Entropy-Regularized Distillation for Early-exit
by: Guidez, Martial, et al.
Published: (2025)
by: Guidez, Martial, et al.
Published: (2025)
Crafting Adversarial Inputs for Large Vision-Language Models Using Black-Box Optimization
by: Guan, Jiwei, et al.
Published: (2026)
by: Guan, Jiwei, et al.
Published: (2026)
Robustness Tokens: Towards Adversarial Robustness of Transformers
by: Pulfer, Brian, et al.
Published: (2025)
by: Pulfer, Brian, et al.
Published: (2025)
Dataset Distillation as Data Compression: A Rate-Utility Perspective
by: Bao, Youneng, et al.
Published: (2025)
by: Bao, Youneng, et al.
Published: (2025)
Taming Diffusion for Dataset Distillation with High Representativeness
by: Zhao, Lin, et al.
Published: (2025)
by: Zhao, Lin, et al.
Published: (2025)
Intriguing Frequency Interpretation of Adversarial Robustness for CNNs and ViTs
by: Chen, Lu, et al.
Published: (2025)
by: Chen, Lu, et al.
Published: (2025)
Improving Generative Adversarial Networks with Self-Distillation
by: Nowinowski, Antoni, et al.
Published: (2026)
by: Nowinowski, Antoni, et al.
Published: (2026)
Robust Dataset Distillation by Matching Adversarial Trajectories
by: Lai, Wei, et al.
Published: (2025)
by: Lai, Wei, et al.
Published: (2025)
Learning Differentially Private Diffusion Models via Stochastic Adversarial Distillation
by: Liu, Bochao, et al.
Published: (2024)
by: Liu, Bochao, et al.
Published: (2024)
Similar Items
-
Dataset Distillation via the Wasserstein Metric
by: Liu, Haoyang, et al.
Published: (2023) -
Towards Understanding Adversarial Transferability in Federated Learning
by: Li, Yijiang, et al.
Published: (2023) -
Approximate Nullspace Augmented Finetuning for Robust Vision Transformers
by: Liu, Haoyang, et al.
Published: (2024) -
Foundation Model-oriented Robustness: Robust Image Model Evaluation with Pretrained Models
by: Zhang, Peiyan, et al.
Published: (2023) -
Distilling Out-of-Distribution Robustness from Vision-Language Foundation Models
by: Zhou, Andy, et al.
Published: (2023)