Results 81 to 90 of about 219,753 (266)

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

RazorNet: Adversarial Training and Noise Training on a Deep Neural Network Fooled by a Shallow Neural Network

Big Data and Cognitive Computing, 2019
In this work, we propose ShallowDeepNet, a novel system architecture that includes a shallow and a deep neural network. The shallow neural network has the duty of data preprocessing and generating adversarial samples. The deep neural network has the duty
Shayan Taheri, Milad Salem, Jiann-Shiun Yuan   +2 more
doaj   +1 more source

Boosting Adversarial Training Using Robust Selective Data Augmentation

International Journal of Computational Intelligence Systems, 2023
Artificial neural networks are currently applied in a wide variety of fields, and they are near to achieving performance similar to humans in many tasks.
Bader Rasheed, Asad Masood Khattak, Adil Khan, Stanislav Protasov, Muhammad Ahmad   +4 more
doaj   +1 more source

INB3P: A Multi‐Modal and Interpretable Co‐Attention Framework Integrating Property‐Aware Explanations and Memory‐Bank Contrastive Fusion for Blood–Brain Barrier Penetrating Peptide Discovery

Advanced Science, EarlyView.
INB3P is a multimodal framework for blood–brain barrier‐penetrating peptide prediction under extreme data scarcity and class imbalance. By combining physicochemical‐guided augmentation, sequence–structure co‐attention, and imbalance‐aware optimization, it improves predictive performance and interpretability.
Jingwei Lv, Qianyang Wu, Jian Liu, Binlu Yang, Yuanhao Li, Junlin Xu, Yajie Meng, Leyi Wei, Zilong Zhang, Quan Zou, Xiulai Li, Feifei Cui   +11 more
wiley   +1 more source

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

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

Adversarial Example Detection and Classification With Asymmetrical Adversarial Training

, 2020
The vulnerabilities of deep neural networks against adversarial examples have become a significant concern for deploying these models in sensitive domains.
Kolouri, Soheil, Rohde, Gustavo K., Yin, Xuwang   +2 more
core  

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

SpaMode: A Broadly Applicable Framework for Deciphering Spatial Multi‐Omics Using Multimodal Mixture of Disentangled Experts

Advanced Science, EarlyView.
SpaMode introduces a versatile framework for spatial multi‐omics integration across vertical, horizontal, and mosaic scenarios. By disentangling modality‐invariant and variant features through a mixture‐of‐experts mechanism, it adaptively reconfigures spatially heterogeneous signals.
Xubin Zheng, Xinlei Huang, Xiang Zhou, Dian Meng, Ziyue Qiao, Zhiyuan Yuan, Lixin Cheng   +6 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