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Initialization Matters for Adversarial Transfer Learning
v1v2 (latest)

Initialization Matters for Adversarial Transfer Learning

10 December 2023
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
31
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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