Results 121 to 130 of about 16,046 (295)
Few-shot Hyperspectral Image Classification using Relational Generative Adversarial Network
Hyperspectral image (HSI) classification is an essential task in remote sensing, but its performance is greatly affected by limited labeled samples.
Guo, Baoqing +4 more
core +1 more source
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.This article reviews the current state of bioinspired soft robotics. The article discusses soft actuators, soft sensors, materials selection, and control methods used in bioinspired soft robotics. It also highlights the challenges and future prospects of this field.
Abhirup Sarker +2 more
wiley +1 more source
Large Language Model‐Based Chatbots in Higher Education
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.The use of large language models (LLMs) in higher education can facilitate personalized learning experiences, advance asynchronized learning, and support instructors, students, and researchers across diverse fields. The development of regulations and guidelines that address ethical and legal issues is essential to ensure safe and responsible adaptation
Defne Yigci +4 more
wiley +1 more source
Discover Artificial IntelligenceOver the past few years, there has been a proliferation of research in the area of generative adversarial networks (GANs). GANs present a novel approach to producing synthetic data in varying fields including medicine, traffic control, text transferring,
John Jenkins, Kaushik Roy
doaj +1 more source
Generating OCT B-Scan DME images using optimized Generative Adversarial Networks (GANs). [PDF]
Heliyon, 2023 Tripathi A, Kumar P, Mayya V, Tulsani A.
europepmc +1 more source
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
GANs for Image Security Applications: A Literature Review
Iraqi Journal of Information & Communication TechnologyGenerative Adversarial Networks (GANs) have earned significant attention in various domains due to their generative model’s compelling ability to generate realistic examples probably drawn from sample distribution.
Mays Y. Mhawi +2 more
doaj +1 more source
A deep learning approach to private data sharing of medical images using conditional generative adversarial networks (GANs). [PDF]
PLoS One, 2023 Sun H +9 more
europepmc +1 more source
Generative Adversarial Networks and Other Generative Models
139192Generative networks are fundamentally different in their aim and methods compared to CNNs for classification, segmentation, or object detection. They have initially been meant not to be an image analysis tool but to produce naturally looking images.
Wenzel, Markus T.
core +1 more source
A game–theoretic approach for Generative Adversarial Networks
, 2020 Generative adversarial networks (GANs) are a class of generative models, known for producing accurate samples. The key feature of GANs is that there are two antagonistic neural networks: the generator and the discriminator.
Franci, Barbara +3 more
core +1 more source