Results 101 to 110 of about 3,499,798 (281)

Batch-in-Batch: a new adversarial training framework for initial perturbation and sample selection

Complex & Intelligent Systems
Adversarial training methods commonly generate initial perturbations that are independent across epochs, and obtain subsequent adversarial training samples without selection.
Yinting Wu, Pai Peng, Bo Cai, Le Li
doaj   +1 more source

Detection of Adversarial Attacks Using Deep Learning and Features Extracted From Interpretability Methods in Industrial Scenarios

IEEE Access
The adversarial training technique has been shown to improve the robustness of Machine Learning and Deep Learning models to adversarial attacks in the Computer Vision field.
Angel Luis Perales Gomez, Lorenzo Fernandez Maimo, Alberto Huertas Celdran, Felix J. Garcia Clemente   +3 more
doaj   +1 more source

Cross‐scale Material‐Structure Synergy for 2D Metamaterials: Toward Customizable Intelligent Electromagnetic Manipulation in Multiphysics Fields

Advanced Science, EarlyView.
Recent advances in metasurface‐enabled low‐observable technologies are reviewed from the perspective of cross‐scale material–structure synergy. Electromagnetic, thermal, optical, and acoustic stealth are highlighted together with dynamic tuning, programmable coding, data‐driven inverse design, artificial intelligence, multispectral compatibility, and ...
Shuhao Wang, Xing Yang, Liping Liu, Yang Peng, Yangyang Wang, Hualiang Lv   +5 more
wiley   +1 more source

Three-Dimensional Reconstruction Pre-Training as a Prior to Improve Robustness to Adversarial Attacks and Spurious Correlation

Entropy
Ensuring robustness of image classifiers against adversarial attacks and spurious correlation has been challenging. One of the most effective methods for adversarial robustness is a type of data augmentation that uses adversarial examples during training.
Yutaro Yamada, Fred Weiying Zhang, Yuval Kluger, Ilker Yildirim   +3 more
doaj   +1 more source

On the Generalization Properties of Adversarial Training

CoRR, 2020
Modern machine learning and deep learning models are shown to be vulnerable when testing data are slightly perturbed. Existing theoretical studies of adversarial training algorithms mostly focus on either adversarial training losses or local convergence properties.
Yue Xing 0002, Qifan Song, Guang Cheng 0003   +2 more
openaire   +4 more sources

Physics Informed Generative Surrogate Learning for Full Wave Validated Real Time Beamforming in Programmable Metasurfaces

Advanced Electronic Materials, EarlyView.
A physics‐guided generative surrogate framework is developed for programmable metasurface beamforming. Mode‐conditioned binary state generation, aperture‐physics prediction, routed residual correction, NSGA‐II optimization, and CST validation are combined to support fast candidate screening and full‐wave beam refinement across single‐beam, dual‐beam ...
Wenqian Liu, Weidong Chen, Chang Chen, Xiang Zhang, Sensong An   +4 more
wiley   +1 more source

Exploring the Impact of Conceptual Bottlenecks on Adversarial Robustness of Deep Neural Networks

IEEE Access
Deep neural networks (DNNs), while powerful, often suffer from a lack of interpretability and vulnerability to adversarial attacks. Concept bottleneck models (CBMs), which incorporate intermediate high-level concepts into the model architecture, promise ...
Bader Rasheed, Mohamed Abdelhamid, Adil Khan, Igor Menezes, Asad Masood Khatak   +4 more
doaj   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Clean, performance‐robust, and performance‐sensitive historical information based adversarial self‐distillation

IET Computer Vision
Adversarial training suffers from poor effectiveness due to the challenging optimisation of loss with hard labels. To address this issue, adversarial distillation has emerged as a potential solution, encouraging target models to mimic the output of the ...
Shuyi Li, Hongchao Hu, Shumin Huo, Hao Liang   +3 more
doaj   +1 more source

On Adversarial Robust Generalization of DNNs for Remote Sensing Image Classification

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Deep neural networks (DNNs)-based deep learning is an important technical support in the task of remote sensing image classification. But DNNs are susceptible to adversarial attacks.
Wei Xue, Yonghao Wang, Shaoquan Zhang, Xiao Zheng, Ping Zhong   +4 more
doaj   +1 more source