Cover Image

Revisiting Min-Max Optimization Problem in Adversarial Training

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ahmadi, Sina Hajer, Bahrami, Hassan
Format:	Preprint
Published:	2024
Subjects:	Computer Vision and Pattern Recognition Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

VisMin: Visual Minimal-Change Understanding
by: Awal, Rabiul, et al.
Published: (2024)

StyDeSty: Min-Max Stylization and Destylization for Single Domain Generalization
by: Liu, Songhua, et al.
Published: (2024)

MMARD: Improving the Min-Max Optimization Process in Adversarial Robustness Distillation
by: Wang, Yuzheng, et al.
Published: (2025)

Adversarial Training against Location-Optimized Adversarial Patches
by: Rao, Sukrut, et al.
Published: (2020)

Adversarial Robustness of AI-Generated Image Detectors in the Real World
by: Mavali, Sina, et al.
Published: (2024)

Preventing Catastrophic Overfitting in Fast Adversarial Training: A Bi-level Optimization Perspective
by: Wang, Zhaoxin, et al.
Published: (2024)

TRIX- Trading Adversarial Fairness via Mixed Adversarial Training
by: Medi, Tejaswini, et al.
Published: (2025)

Enhancing Adversarial Robustness via Uncertainty-Aware Distributional Adversarial Training
by: Dong, Junhao, et al.
Published: (2024)

What is Adversarial Training for Diffusion Models?
by: Rosaria, Briglia Maria, et al.
Published: (2025)

Dynamic Label Adversarial Training for Deep Learning Robustness Against Adversarial Attacks
by: Liu, Zhenyu, et al.
Published: (2024)

Contrastive Adversarial Training for Unsupervised Domain Adaptation
by: Chen, Jiahong, et al.
Published: (2024)

Do Counterfactual Examples Complicate Adversarial Training?
by: Yeats, Eric, et al.
Published: (2024)

Adversarial Training from Mean Field Perspective
by: Kumano, Soichiro, et al.
Published: (2025)

Compositional Adversarial Training for Robust Visual Watermarking
by: Satheesh, Anirudh, et al.
Published: (2026)

Understanding Adversarial Training with Energy-based Models
by: Mirza, Mujtaba Hussain, et al.
Published: (2025)

Model Interpretability and Rationale Extraction by Input Mask Optimization
by: Brinner, Marc, et al.
Published: (2025)

Revisiting Semi-supervised Adversarial Robustness via Noise-aware Online Robust Distillation
by: Wu, Tsung-Han, et al.
Published: (2024)

RANSAC Revisited: An Improved Algorithm for Robust Subspace Recovery under Adversarial and Noisy Corruptions
by: Chen, Guixian, et al.
Published: (2025)

Narrowing Class-Wise Robustness Gaps in Adversarial Training
by: Amerehi, Fatemeh, et al.
Published: (2025)

Nested Annealed Training Scheme for Generative Adversarial Networks
by: Wan, Chang, et al.
Published: (2025)

GReAT: A Graph Regularized Adversarial Training Method
by: Bayram, Samet, et al.
Published: (2023)

CDAN: Convolutional dense attention-guided network for low-light image enhancement
by: Shakibania, Hossein, et al.
Published: (2023)

MEAT: Median-Ensemble Adversarial Training for Improving Robustness and Generalization
by: Hu, Zhaozhe, et al.
Published: (2024)

Improving Fast Adversarial Training Paradigm: An Example Taxonomy Perspective
by: Gui, Jie, et al.
Published: (2024)

Topology-preserving Adversarial Training for Alleviating Natural Accuracy Degradation
by: Mi, Xiaoyue, et al.
Published: (2023)

Min Generalized Sliced Gromov Wasserstein: A Scalable Path to Gromov Wasserstein
by: Shahbazi, Ashkan, et al.
Published: (2026)

Boosting Adversarial Training via Fisher-Rao Norm-based Regularization
by: Yin, Xiangyu, et al.
Published: (2024)

FAIR-TAT: Improving Model Fairness Using Targeted Adversarial Training
by: Medi, Tejaswini, et al.
Published: (2024)

LTD: Low Temperature Distillation for Gradient Masking-free Adversarial Training
by: Chen, Erh-Chung, et al.
Published: (2021)

Taming the Long Tail: Rebalancing Adversarial Training via Adaptive Perturbation
by: Zhang, Lilin, et al.
Published: (2026)

Medical Image Classification on Imbalanced Data Using ProGAN and SMA-Optimized ResNet: Application to COVID-19
by: Jahromi, Sina, et al.
Published: (2025)

Together, Then Apart: Revisiting Multimodal Survival Analysis via a Min-Max Perspective
by: Liu, Wenjing, et al.
Published: (2025)

MaxInfo: A Training-Free Key-Frame Selection Method Using Maximum Volume for Enhanced Video Understanding
by: Li, Pengyi, et al.
Published: (2025)

Criticality Leveraged Adversarial Training (CLAT) for Boosted Performance via Parameter Efficiency
by: Gopal, Bhavna, et al.
Published: (2024)

Federated Hybrid Training and Self-Adversarial Distillation: Towards Robust Edge Networks
by: Qiao, Yu, et al.
Published: (2024)

Improving Accuracy-robustness Trade-off via Pixel Reweighted Adversarial Training
by: Zhang, Jiacheng, et al.
Published: (2024)

Robust Experts: the Effect of Adversarial Training on CNNs with Sparse Mixture-of-Experts Layers
by: Pavlitska, Svetlana, et al.
Published: (2025)

Towards Fair Class-wise Robustness: Class Optimal Distribution Adversarial Training
by: Zhi, Hongxin, et al.
Published: (2025)

Explainability-Driven Leaf Disease Classification Using Adversarial Training and Knowledge Distillation
by: Echim, Sebastian-Vasile, et al.
Published: (2023)

Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Need
by: Zhou, Da-Wei, et al.
Published: (2023)