Summary of Boosting the Transferability Of Adversarial Examples Via Local Mixup and Adaptive Step Size, by Junlin Liu and Xinchen Lyu
Boosting the Transferability of Adversarial Examples via Local Mixup and Adaptive Step Sizeby Junlin Liu,…
Summary of Enhancing Targeted Transferability Via Feature Space Fine-tuning, by Hui Zeng et al.
Enhancing targeted transferability via feature space fine-tuningby Hui Zeng, Biwei Chen, Anjie PengFirst submitted to…
Summary of Evaluating the Adversarial Robustness Of Detection Transformers, by Amirhossein Nazeri et al.
Evaluating the Adversarial Robustness of Detection Transformersby Amirhossein Nazeri, Chunheng Zhao, Pierluigi PisuFirst submitted to…
Summary of Towards Graph Foundation Models: Learning Generalities Across Graphs Via Task-trees, by Zehong Wang et al.
Towards Graph Foundation Models: Learning Generalities Across Graphs via Task-Treesby Zehong Wang, Zheyuan Zhang, Tianyi…
Summary of Multi-source Unsupervised Domain Adaptation with Prototype Aggregation, by Min Huang et al.
Multi-Source Unsupervised Domain Adaptation with Prototype Aggregationby Min Huang, Zifeng Xie, Bo Sun, Ning WangFirst…
Summary of Trustworthy Transfer Learning: a Survey, by Jun Wu and Jingrui He
Trustworthy Transfer Learning: A Surveyby Jun Wu, Jingrui HeFirst submitted to arxiv on: 18 Dec…
Summary of Transferable and Forecastable User Targeting Foundation Model, by Bin Dou et al.
Transferable and Forecastable User Targeting Foundation Modelby Bin Dou, Baokun Wang, Yun Zhu, Xiaotong Lin,…
Summary of Svasp: Self-versatility Adversarial Style Perturbation For Cross-domain Few-shot Learning, by Wenqian Li et al.
SVasP: Self-Versatility Adversarial Style Perturbation for Cross-Domain Few-Shot Learningby Wenqian Li, Pengfei Fang, Hui XueFirst…
Summary of Grimm: a Plug-and-play Perturbation Rectifier For Graph Neural Networks Defending Against Poisoning Attacks, by Ao Liu et al.
Grimm: A Plug-and-Play Perturbation Rectifier for Graph Neural Networks Defending against Poisoning Attacksby Ao Liu,…
Summary of Node-advgan: Improving the Transferability and Perceptual Similarity Of Adversarial Examples by Dynamic-system-driven Adversarial Generative Model, By Xinheng Xie et al.
NODE-AdvGAN: Improving the transferability and perceptual similarity of adversarial examples by dynamic-system-driven adversarial generative modelby…