Adversarial training - Open Access .click

Results 131 to 140 of about 34,476 (259)

Evaluation of GAN-Based Model for Adversarial Training. [PDF]

Sensors (Basel), 2023
Zhao W, Mahmoud QH, Alwidian S.
europepmc +1 more source

Zero Watermarking Using Convolutional Additive Self‐Attention Vision Transformer and Discrete Wavelet Transform‐Variance‐Based Feature Descriptor for Medical Image Security in Mobile Healthcare Services

Advanced Intelligent Systems, EarlyView.
A zero‐watermarking algorithm that combines a refined convolutional additive self‐attention vision transformer (CAS‐ViT) with a discrete wavelet transform variance‐based feature descriptor (DVFD) is proposed for protecting the privacy of medical images in mobile healthcare services.
Pei Liu +6 more
wiley +1 more source

Diversifying Emotional Dialogue Generation via Selective Adversarial Training. [PDF]

Sensors (Basel), 2023
Li B, Zhao H, Zhang Z.
europepmc +1 more source

Slack Federated Adversarial Training

IEEE Transactions on Pattern Analysis and Machine Intelligence
Security and privacy concerns in real-world applications have led to the development of adversarially robust federated models. Previous works mainly target overcoming the adaptability constraints regarding communication and computation costs. However, the straightforward combination of adversarial training and federated learning might lead to undesired
Jianing Zhu +5 more
openaire +2 more sources

Adversarial Erasing Enhanced Multiple Instance Learning (siMILe): Discriminative Identification of Oligomeric Protein Structures in Single Molecule Localization Microscopy

Advanced Intelligent Systems, EarlyView.
Hallgrimson et al. introduce a machine learning algorithm, siMILe, that takes features of single‐molecule localization microscopy localization clusters (e.g., size and sphericity) and finds the clusters that are associated with certain cell conditions (such as differential protein expression or drug treatment).
Christian Hallgrimson +8 more
wiley +1 more source

Feature separation and adversarial training for the patient-independent detection of epileptic seizures. [PDF]

Front Comput Neurosci, 2023
Yang Y, Li F, Qin X, Wen H, Lin X, Huang D. +5 more
europepmc +1 more source

Retinal Vessel Segmentation: A Comprehensive Review From Classical Methods to Deep Learning Advances (1982–2025)

Advanced Intelligent Systems, EarlyView.
Four decades of retinal vessel segmentation research (1982–2025) are synthesized, spanning classical image processing, machine learning, and deep learning paradigms. A meta‐analysis of 428 studies establishes a unified taxonomy and highlights performance trends, generalization capabilities, and clinical relevance.
Avinash Bansal +6 more
wiley +1 more source

Between-Class Adversarial Training for Improving Adversarial Robustness of Image Classification. [PDF]

Sensors (Basel), 2023
Wang D, Jin W, Wu Y.
europepmc +1 more source

An Intelligent Feature Engineering‐Driven Hybrid Framework for Adversarial Domain Name System Tunneling Detection

Advanced Intelligent Systems, EarlyView.
This study presents a novel framework that enhances the reliability of DNS traffic monitoring using a hybrid long short‐term memory‐deep neural network (LSMT‐DNN) architecture, enabling robust detection of adversarial DNS tunneling. The proposed framework leverages feature extraction from DNS traffic patterns, including domain request sequences, query ...
Ahmad Almadhor +5 more
wiley +1 more source

An adversarial training framework for mitigating algorithmic biases in clinical machine learning. [PDF]

NPJ Digit Med, 2023
Yang J +4 more
europepmc +1 more source

adversarial attacks
deep learning
adversarial attack

adversarial robustness
robustness
adversarial machine learning

data augmentation
adversarial examples
image classification