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Initialization Matters for Adversarial Transfer Learning
v1v2 (latest)

Initialization Matters for Adversarial Transfer Learning

Andong Hua
Jindong Gu
Zhiyu Xue
Nicholas Carlini
Eric Wong
Yao Qin
    AAML
ArXiv (abs)PDFHTMLGithub (7★)

Papers citing "Initialization Matters for Adversarial Transfer Learning"

5 / 5 papers shown
Title
Bridging Distribution Shift and AI Safety: Conceptual and Methodological Synergies
Bridging Distribution Shift and AI Safety: Conceptual and Methodological Synergies
Chenruo Liu
Kenan Tang
Yao Qin
Qi Lei
28
0
0
28 May 2025
On the Robustness Tradeoff in Fine-Tuning
On the Robustness Tradeoff in Fine-Tuning
Kunyang Li
Jean-Charles Noirot Ferrand
Ryan Sheatsley
Blaine Hoak
Yohan Beugin
Eric Pauley
Patrick McDaniel
91
0
0
19 Mar 2025
Conflict-Aware Adversarial Training
Conflict-Aware Adversarial Training
Zhiyu Xue
Haohan Wang
Yao Qin
Ramtin Pedarsani
AAML
68
0
0
21 Oct 2024
Implicit to Explicit Entropy Regularization: Benchmarking ViT Fine-tuning under Noisy Labels
Implicit to Explicit Entropy Regularization: Benchmarking ViT Fine-tuning under Noisy Labels
Maria Marrium
Arif Mahmood
Mohammed Bennamoun
NoLaAAML
94
0
0
05 Oct 2024
As Firm As Their Foundations: Can open-sourced foundation models be used to create adversarial examples for downstream tasks?
As Firm As Their Foundations: Can open-sourced foundation models be used to create adversarial examples for downstream tasks?
Anjun Hu
Jindong Gu
Francesco Pinto
Konstantinos Kamnitsas
Philip Torr
AAMLSILM
86
5
0
19 Mar 2024

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