Results 151 to 160 of about 39,632 (294)

Uncertainty‐Guided Selective Adaptation Enables Cross‐Platform Predictive Fluorescence Microscopy

Advanced Intelligent Discovery, EarlyView.
Deep learning models often fail when transferred to new microscopes. A novel framework overcomes this by selectively adapting the early layers governing low‐level image statistics, while freezing deep layers that encode morphology. This uncertainty‐guided approach enables robust, label‐free virtual staining across diverse systems, democratizing ...
Kai‐Wen K. Yang, Andrew Bai, Alexandra Bermudez, Yunqi Hong, Zoe Latham, Iris Sloan, Michael Liu, Vishrut Goyal, Cho‐Jui Hsieh, Neil Y. C. Lin   +9 more
wiley   +1 more source

A Tunnel Crack Segmentation and Recognition Algorithm Using SPGD-and-Generative Adversarial Network Fusion

Sensors
In order to improve the recognition ability of tunnel cracks in the UAV platform with a vision imaging system in the UAV platform with a vision imaging system, this paper proposes a tunnel crack segmentation algorithm using SPGD-and-generative ...
Wei Sun, Xiaohu Liu, Zhiyong Lei
doaj   +1 more source

Regional attention generative adversarial network

Electronics Letters, 2019
In this Letter, the authors propose a novel attention mechanism combined with a classical generative adversarial network (GAN) model to improve the visual quality of generated samples. This novel attention model is named regional attention GAN.
Wei Wang, Haifeng Hu, Yi Huang, Chongchong Ruan, Dihu Chen   +4 more
doaj   +1 more source

Evaluating the Effectiveness of Training a Generative Adversarial Network Through Transfer Learning for the Purpose of Completing CT Images

The experiment was conducted via a three-step approach. Firstly, hyperparameter tuning was completed to broadly support the image completion GAN’s ability to complete greyscale images.
Fisher, Roman
core  

Detracking Autoencoding Conditional Generative Adversarial Network: Improved Generative Adversarial Network Method for Tabular Missing Value Imputation

Due to various reasons, such as limitations in data collection and interruptions in network transmission, gathered data often contain missing values.
Jingrui Liu, Zixin Duan, Jingxuan Zhong, Yunfei Yin, Xinkai Hu   +4 more
core   +1 more source

Single-image de-raining with feature-supervised generative adversarial network

, 2019
De-raining, which aims at rain-steak removal from images, is a practical task in computer vision. However, it is difficult due to its ill-posed nature.
Wu, Fuxiang, Zhou, Mengchu, Xiang, Peng, Wang, Lei, Cheng, Jun   +4 more
core   +1 more source

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source

A Review on Recent Trends of Bioinspired Soft Robotics: Actuators, Control Methods, Materials Selection, Sensors, Challenges, and Future Prospects

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, Tamzid Ul Islam, Md. Robiul Islam   +2 more
wiley   +1 more source

A Deep Learning-Based Two-Branch Generative Adversarial Network for Image De-Raining

Sensors
Raindrops can scatter and absorb light, causing images to become blurry or distorted. To improve image quality by reducing the impact of raindrops, this paper proposes a novel generative adversarial network for image de-raining. The network comprises two
Liquan Zhao, Jie Long, Tie Zhong
doaj   +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, Merve Eryilmaz, Ail K. Yetisen, Savas Tasoglu, Aydogan Ozcan   +4 more
wiley   +1 more source