Adversarial training on simplified Vision Transformers achieves benign overfitting with near-zero robust loss and generalization error when signal-to-noise ratio and perturbation budget meet specific conditions.
arXiv preprint arXiv:2302.06015 , year=
3 Pith papers cite this work. Polarity classification is still indexing.
citation-role summary
citation-polarity summary
years
2026 3verdicts
UNVERDICTED 3roles
background 1polarities
unclear 1representative citing papers
Activation prompts on intermediate layers outperform input-level visual prompting and parameter-efficient fine-tuning in accuracy and efficiency across 29 datasets.
A radiomics-guided hybrid Vision Transformer integrates pixel embeddings with interpretable radiomic features in a multimodal Cox model for survival analysis, yielding competitive discrimination and clinically meaningful attention maps on COVID-19 chest X-ray data.
citing papers explorer
-
Benign Overfitting in Adversarial Training for Vision Transformers
Adversarial training on simplified Vision Transformers achieves benign overfitting with near-zero robust loss and generalization error when signal-to-noise ratio and perturbation budget meet specific conditions.
-
Visual prompting reimagined: The power of the Activation Prompts
Activation prompts on intermediate layers outperform input-level visual prompting and parameter-efficient fine-tuning in accuracy and efficiency across 29 datasets.
-
Radiomics-Guided Vision Transformers for Survival Analysis
A radiomics-guided hybrid Vision Transformer integrates pixel embeddings with interpretable radiomic features in a multimodal Cox model for survival analysis, yielding competitive discrimination and clinically meaningful attention maps on COVID-19 chest X-ray data.