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Found 6 papers, 4 papers with code
SpecFormer: Guarding Vision Transformer Robustness via Maximum Singular Value Penalization
In this study, we address this gap by introducing SpecFormer, specifically designed to enhance ViTs' resilience against adversarial attacks, with support from carefully derived theoretical guarantees.
Frustratingly Easy Model Generalization by Dummy Risk Minimization
Empirical risk minimization (ERM) is a fundamental machine learning paradigm.
Improving Generalization of Adversarial Training via Robust Critical Fine-Tuning
The core idea of RiFT is to exploit the redundant capacity for robustness by fine-tuning the adversarially trained model on its non-robust-critical module.
Deep into The Domain Shift: Transfer Learning through Dependence Regularization
This paper proposes a new domain adaptation approach in which one can measure the differences in the internal dependence structure separately from those in the marginals.
FedCLIP: Fast Generalization and Personalization for CLIP in Federated Learning
Concretely, we design an attention-based adapter for the large model, CLIP, and the rest operations merely depend on adapters.
On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective
In this paper, we conduct a thorough evaluation of the robustness of ChatGPT from the adversarial and out-of-distribution (OOD) perspective.
