Saved in:
| Main Authors: | Yu, Chaojian, Shi, Xiaolong, Yu, Jun, Han, Bo, Liu, Tongliang |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2310.00607 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
On the Over-Memorization During Natural, Robust and Catastrophic Overfitting
by: Lin, Runqi, et al.
Published: (2023)
by: Lin, Runqi, et al.
Published: (2023)
Layer-Aware Analysis of Catastrophic Overfitting: Revealing the Pseudo-Robust Shortcut Dependency
by: Lin, Runqi, et al.
Published: (2024)
by: Lin, Runqi, et al.
Published: (2024)
Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples Regularization
by: Lin, Runqi, et al.
Published: (2024)
by: Lin, Runqi, et al.
Published: (2024)
MeGU: Machine-Guided Unlearning with Target Feature Disentanglement
by: Wang, Haoyu, et al.
Published: (2026)
by: Wang, Haoyu, et al.
Published: (2026)
Rethinking Deep Research from the Perspective of Web Content Distribution Matching
by: Yu, Zixuan, et al.
Published: (2026)
by: Yu, Zixuan, et al.
Published: (2026)
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)
Stealthy and Adjustable Text-Guided Backdoor Attacks on Multimodal Pretrained Models
by: Zhang, Yiyang, et al.
Published: (2026)
by: Zhang, Yiyang, et al.
Published: (2026)
Generative Model Inversion Through the Lens of the Manifold Hypothesis
by: Peng, Xiong, et al.
Published: (2025)
by: Peng, Xiong, et al.
Published: (2025)
Robust Training of Federated Models with Extremely Label Deficiency
by: Zhang, Yonggang, et al.
Published: (2024)
by: Zhang, Yonggang, et al.
Published: (2024)
Mitigating Label Noise on Graph via Topological Sample Selection
by: Wu, Yuhao, et al.
Published: (2024)
by: Wu, Yuhao, et al.
Published: (2024)
Enhancing Sample Selection Against Label Noise by Cutting Mislabeled Easy Examples
by: Yuan, Suqin, et al.
Published: (2025)
by: Yuan, Suqin, et al.
Published: (2025)
Mitigating Structural Overfitting: A Distribution-Aware Rectification Framework for Missing Feature Imputation
by: Song, Yifan, et al.
Published: (2025)
by: Song, Yifan, et al.
Published: (2025)
Understanding Generalization in Transformers: Error Bounds and Training Dynamics Under Benign and Harmful Overfitting
by: Zhang, Yingying, et al.
Published: (2025)
by: Zhang, Yingying, et al.
Published: (2025)
Overfitting in Adaptive Robust Optimization
by: Zhu, Karl, et al.
Published: (2025)
by: Zhu, Karl, et al.
Published: (2025)
Tackling Noisy Labels with Network Parameter Additive Decomposition
by: Wang, Jingyi, et al.
Published: (2024)
by: Wang, Jingyi, et al.
Published: (2024)
How to Mitigate Overfitting in Weak-to-strong Generalization?
by: Shi, Junhao, et al.
Published: (2025)
by: Shi, Junhao, et al.
Published: (2025)
Model Inversion Attacks: A Survey of Approaches and Countermeasures
by: Zhou, Zhanke, et al.
Published: (2024)
by: Zhou, Zhanke, et al.
Published: (2024)
A Survey of Weight Space Learning: Understanding, Representation, and Generation
by: Han, Xiaolong, et al.
Published: (2026)
by: Han, Xiaolong, et al.
Published: (2026)
Instance-dependent Early Stopping
by: Yuan, Suqin, et al.
Published: (2025)
by: Yuan, Suqin, et al.
Published: (2025)
2-in-1 Accelerator: Enabling Random Precision Switch for Winning Both Adversarial Robustness and Efficiency
by: Fu, Yonggan, et al.
Published: (2021)
by: Fu, Yonggan, et al.
Published: (2021)
Provable Robust Overfitting Mitigation in Wasserstein Distributionally Robust Optimization
by: Liu, Shuang, et al.
Published: (2025)
by: Liu, Shuang, et al.
Published: (2025)
What If the Input is Expanded in OOD Detection?
by: Zhang, Boxuan, et al.
Published: (2024)
by: Zhang, Boxuan, et al.
Published: (2024)
The Surprising Harmfulness of Benign Overfitting for Adversarial Robustness
by: Hao, Yifan, et al.
Published: (2024)
by: Hao, Yifan, et al.
Published: (2024)
Neural auto-designer for enhanced quantum kernels
by: Lei, Cong, et al.
Published: (2024)
by: Lei, Cong, et al.
Published: (2024)
SubFlow: Sub-mode Conditioned Flow Matching for Diverse One-Step Generation
by: Lin, Yexiong, et al.
Published: (2026)
by: Lin, Yexiong, et al.
Published: (2026)
Testing for Overfitting
by: Schmidt, James
Published: (2023)
by: Schmidt, James
Published: (2023)
Residual Stream Analysis of Overfitting And Structural Disruptions
by: Liu, Quan, et al.
Published: (2026)
by: Liu, Quan, et al.
Published: (2026)
Envisioning Outlier Exposure by Large Language Models for Out-of-Distribution Detection
by: Cao, Chentao, et al.
Published: (2024)
by: Cao, Chentao, et al.
Published: (2024)
Theoretical Analysis of Robust Overfitting for Wide DNNs: An NTK Approach
by: Fu, Shaopeng, et al.
Published: (2023)
by: Fu, Shaopeng, et al.
Published: (2023)
Relative Overfitting and Accept-Reject Framework
by: Liu, Yanxin, et al.
Published: (2025)
by: Liu, Yanxin, et al.
Published: (2025)
Fairness Overfitting in Machine Learning: An Information-Theoretic Perspective
by: Laakom, Firas, et al.
Published: (2025)
by: Laakom, Firas, et al.
Published: (2025)
Unraveling the Impact of Heterophilic Structures on Graph Positive-Unlabeled Learning
by: Wu, Yuhao, et al.
Published: (2024)
by: Wu, Yuhao, et al.
Published: (2024)
Towards Effective Evaluations and Comparisons for LLM Unlearning Methods
by: Wang, Qizhou, et al.
Published: (2024)
by: Wang, Qizhou, et al.
Published: (2024)
BrokenBind: Universal Modality Exploration beyond Dataset Boundaries
by: Huang, Zhuo, et al.
Published: (2026)
by: Huang, Zhuo, et al.
Published: (2026)
Is Gradient Ascent Really Necessary? Memorize to Forget for Machine Unlearning
by: Huang, Zhuo, et al.
Published: (2026)
by: Huang, Zhuo, et al.
Published: (2026)
Enhancing One-Shot Federated Learning Through Data and Ensemble Co-Boosting
by: Dai, Rong, et al.
Published: (2024)
by: Dai, Rong, et al.
Published: (2024)
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)
by: Li, Binghui, et al.
Published: (2023)
Adjusted Overfitting Regression
by: Wilson, Dylan
Published: (2024)
by: Wilson, Dylan
Published: (2024)
Overfitting In Contrastive Learning?
by: Rabin, Zachary, et al.
Published: (2024)
by: Rabin, Zachary, et al.
Published: (2024)
A3C-S: Automated Agent Accelerator Co-Search towards Efficient Deep Reinforcement Learning
by: Fu, Yonggan, et al.
Published: (2021)
by: Fu, Yonggan, et al.
Published: (2021)
Similar Items
-
On the Over-Memorization During Natural, Robust and Catastrophic Overfitting
by: Lin, Runqi, et al.
Published: (2023) -
Layer-Aware Analysis of Catastrophic Overfitting: Revealing the Pseudo-Robust Shortcut Dependency
by: Lin, Runqi, et al.
Published: (2024) -
Eliminating Catastrophic Overfitting Via Abnormal Adversarial Examples Regularization
by: Lin, Runqi, et al.
Published: (2024) -
MeGU: Machine-Guided Unlearning with Target Feature Disentanglement
by: Wang, Haoyu, et al.
Published: (2026) -
Rethinking Deep Research from the Perspective of Web Content Distribution Matching
by: Yu, Zixuan, et al.
Published: (2026)