4 citations · 12 across the 11 of their papers we have counts for
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Anchoring Adversarial Trajectories to Data Manifolds: A Bilevel Transfer Optimization Framework
Yaohua Liu, Yifan Guo, Jiaxin Gao
A key bottleneck in adversarial transfer is a trajectory-level geometric disconnect: ambient gradients often drift away from the intrinsic data manifold, causing surrogate-specific…
Learning with Bilevel-Minimax Optimization for Efficient and Reliable Transfer Attacks
Yaohua Liu, Yifan Guo, Jiaxin Gao
Transfer-based adversarial attacks craft adversarial examples using surrogate models to mislead black-box victim models. Beyond perturbation generation, transferability is fundamen…
Advancing Generalized Transfer Attack with Initialization Derived Bilevel Optimization and Dynamic Sequence Truncation
Yaohua Liu, Jiaxin Gao, Xuan Liu +3
Transfer attacks generate significant interest for real-world black-box applications by crafting transferable adversarial examples through surrogate models. Whereas, existing works…
Learn from the Past: A Proxy Guided Adversarial Defense Framework with Self Distillation Regularization
Yaohua Liu, Jiaxin Gao, Xianghao Jiao +3
Adversarial Training (AT), pivotal in fortifying the robustness of deep learning models, is extensively adopted in practical applications. However, prevailing AT methods, relying o…
TgDLF2.0: Theory-guided deep-learning for electrical load forecasting via Transformer and transfer learning
Jiaxin Gao, Wenbo Hu, Dongxiao Zhang +1
Electrical energy is essential in today's society. Accurate electrical load forecasting is beneficial for better scheduling of electricity generation and saving electrical energy.…
Revisiting GANs by Best-Response Constraint: Perspective, Methodology, and Application
Risheng Liu, Jiaxin Gao, Xuan Liu +1
In past years, the minimax type single-level optimization formulation and its variations have been widely utilized to address Generative Adversarial Networks (GANs). Unfortunately,…