4 citations · 12 across the 11 of their papers we have counts for
13 papers
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…
AutoPSO: A Meta-framework for Automated Particle Swarm Optimization
Xinmeng Yu, Jiaxin Gao, Jianguo Zhang +2
Particle swarm optimization (PSO) is a widely used metaheuristic, prized for its simplicity and small parameter set. Although decades of research have produced numerous PSO variant…
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…
LoLiSRFlow: Joint Single Image Low-light Enhancement and Super-resolution via Cross-scale Transformer-based Conditional Flow
Ziyu Yue, Jiaxin Gao, Sihan Xie +2
The visibility of real-world images is often limited by both low-light and low-resolution, however, these issues are only addressed in the literature through Low-Light Enhancement…
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…
Diving into Darkness: A Dual-Modulated Framework for High-Fidelity Super-Resolution in Ultra-Dark Environments
Jiaxin Gao, Ziyu Yue, Yaohua Liu +3
Super-resolution tasks oriented to images captured in ultra-dark environments is a practical yet challenging problem that has received little attention. Due to uneven illumination…
Learning with Constraint Learning: New Perspective, Solution Strategy and Various Applications
Risheng Liu, Jiaxin Gao, Xuan Liu +1
The complexity of learning problems, such as Generative Adversarial Network (GAN) and its variants, multi-task and meta-learning, hyper-parameter learning, and a variety of real-wo…
PEARL: Preprocessing Enhanced Adversarial Robust Learning of Image Deraining for Semantic Segmentation
Xianghao Jiao, Yaohua Liu, Jiaxin Gao +3
In light of the significant progress made in the development and application of semantic segmentation tasks, there has been increasing attention towards improving the robustness of…
Motion-Scenario Decoupling for Rat-Aware Video Position Prediction: Strategy and Benchmark
Xiaofeng Liu, Jiaxin Gao, Yaohua Liu +2
Recently significant progress has been made in human action recognition and behavior prediction using deep learning techniques, leading to improved vision-based semantic understand…
Advancing Unsupervised Low-light Image Enhancement: Noise Estimation, Illumination Interpolation, and Self-Regulation
Xiaofeng Liu, Jiaxin Gao, Xin Fan +1
Contemporary Low-Light Image Enhancement (LLIE) techniques have made notable advancements in preserving image details and enhancing contrast, achieving commendable results on speci…
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,…