4 citations · 16 across the 19 of their papers we have counts for
6 papers · 1 filter
AdvST: Revisiting Data Augmentations for Single Domain Generalization
Guangtao Zheng, Mengdi Huai, Aidong Zhang
Single domain generalization (SDG) aims to train a robust model against unknown target domain shifts using data from a single source domain. Data augmentation has been proven an ef…
Improving Interpretation Faithfulness for Vision Transformers
Lijie Hu, Yixin Liu, Ninghao Liu +3
Vision Transformers (ViTs) have achieved state-of-the-art performance for various vision tasks. One reason behind the success lies in their ability to provide plausible innate expl…
Automated Natural Language Explanation of Deep Visual Neurons with Large Models
Chenxu Zhao, Wei Qian, Yucheng Shi +2
Deep neural networks have exhibited remarkable performance across a wide range of real-world tasks. However, comprehending the underlying reasons for their effectiveness remains a…
MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation
Yuan Zhong, Suhan Cui, Jiaqi Wang +7
Health risk prediction is one of the fundamental tasks under predictive modeling in the medical domain, which aims to forecast the potential health risks that patients may face in…
White-box Membership Inference Attacks against Diffusion Models
Yan Pang, Tianhao Wang, Xuhui Kang +2
Diffusion models have begun to overshadow GANs and other generative models in industrial applications due to their superior image generation performance. The complex architecture o…
Inductive Graph Unlearning
Cheng-Long Wang, Mengdi Huai, Di Wang
As a way to implement the "right to be forgotten" in machine learning, \textit{machine unlearning} aims to completely remove the contributions and information of the samples to be…