"model robustness" Papers
19 papers found
Aligning Visual Contrastive learning models via Preference Optimization
Amirabbas Afzali, Borna khodabandeh, Ali Rasekh et al.
Learning Diverse Attacks on Large Language Models for Robust Red-Teaming and Safety Tuning
Seanie Lee, Minsu Kim, Lynn Cherif et al.
Modality-Aware SAM: Sharpness-Aware-Minimization Driven Gradient Modulation for Harmonized Multimodal Learning
Hossein Rajoli Nowdeh, Jie Ji, Xiaolong Ma et al.
Optimal Control for Transformer Architectures: Enhancing Generalization, Robustness and Efficiency
Kelvin Kan, Xingjian Li, Benjamin Zhang et al.
Perturb a Model, Not an Image: Towards Robust Privacy Protection via Anti-Personalized Diffusion Models
Tae-Young Lee, Juwon Seo, Jong Hwan Ko et al.
Remarkable Robustness of LLMs: Stages of Inference?
Vedang Lad, Jin Hwa Lee, Wes Gurnee et al.
Resolution Attack: Exploiting Image Compression to Deceive Deep Neural Networks
Wangjia Yu, Xiaomeng Fu, Qiao Li et al.
Rethinking Evaluation of Infrared Small Target Detection
Youwei Pang, Xiaoqi Zhao, Lihe Zhang et al.
Topological Zigzag Spaghetti for Diffusion-based Generation and Prediction on Graphs
Yuzhou Chen, Yulia Gel
TransferBench: Benchmarking Ensemble-based Black-box Transfer Attacks
Fabio Brau, Maura Pintor, Antonio Cinà et al.
Beyond the Federation: Topology-aware Federated Learning for Generalization to Unseen Clients
Mengmeng Ma, Tang Li, Xi Peng
Energy-based Backdoor Defense without Task-Specific Samples and Model Retraining
Yudong Gao, Honglong Chen, Peng Sun et al.
HarmBench: A Standardized Evaluation Framework for Automated Red Teaming and Robust Refusal
Mantas Mazeika, Long Phan, Xuwang Yin et al.
Improving SAM Requires Rethinking its Optimization Formulation
Wanyun Xie, Fabian Latorre, Kimon Antonakopoulos et al.
MM-SafetyBench: A Benchmark for Safety Evaluation of Multimodal Large Language Models
Xin Liu, Yichen Zhu, Jindong Gu et al.
Revealing the Dark Secrets of Extremely Large Kernel ConvNets on Robustness
Honghao Chen, Zhang Yurong, xiaokun Feng et al.
Robustness of Deep Learning for Accelerated MRI: Benefits of Diverse Training Data
Kang Lin, Reinhard Heckel
Unraveling Batch Normalization for Realistic Test-Time Adaptation
Zixian Su, Jingwei Guo, Kai Yao et al.
Why Larger Language Models Do In-context Learning Differently?
Zhenmei Shi, Junyi Wei, Zhuoyan Xu et al.