Results 61 to 70 of about 39,370 (305)

Robust Generative Adversarial Network

CoRR, 2020
Generative adversarial networks (GANs) are powerful generative models, but usually suffer from instability and generalization problem which may lead to poor generations. Most existing works focus on stabilizing the training of the discriminator while ignoring the generalization properties.
Shufei Zhang, Zhuang Qian, Kaizhu Huang, Jimin Xiao, Yuan He   +4 more
openaire   +2 more sources

3D Printing Innovations in Polymeric Porous and Patterned Architecture

Advanced Functional Materials, EarlyView.
Polymeric foams occupy a unique structural space between dense solids and open networks, where engineered void fraction governs mechanical compliance, thermal resistance, and mass transport. Additive manufacturing now enables precise spatial control over cellular architecture, unlocking designer foam structures across applications spanning crash ...
Dhanush Patil, Sri Vaishnavi Thummalapalli, Clarissa Westover, Arunachalam Ramanathan, M. Taylor Sobzcak, Roumika Patil, Qixuan Xiang, Tiantian Li, Sui Yang, Timothy E. Long, Hongyue Sun, Xianqiao Wang, Yang Liu, Kenan Song   +13 more
wiley   +1 more source

Generative Adversarial Networks in Speech Enhancement: A Survey

IEEE Access
Generative adversarial networks are a powerful type of model in deep learning. They have been successfully applied within different domains. This review focuses on the usage of generative adversarial networks for speech enhancement.
Justina Ramonaite, Grazina Korvel, Gintautas Tamulevicius   +2 more
doaj   +1 more source

An Adaptive Generative Adversarial Network for Cardiac Segmentation from X-ray Chest Radiographs

Applied Sciences, 2020
Medical image segmentation is a classic challenging problem. The segmentation of parts of interest in cardiac medical images is a basic task for cardiac image diagnosis and guided surgery.
Xiaochang Wu, Xiaolin Tian
doaj   +1 more source

Coupled Generative Adversarial Networks

CoRR, 2016
We propose coupled generative adversarial network (CoGAN) for learning a joint distribution of multi-domain images. In contrast to the existing approaches, which require tuples of corresponding images in different domains in the training set, CoGAN can learn a joint distribution without any tuple of corresponding images.
Ming-Yu Liu 0001, Oncel Tuzel
openaire   +3 more sources

Generating Ampicillin-Level Antimicrobial Peptides with Activity-Aware Generative Adversarial Networks

, 2020
Antimicrobial peptides are a potential solution to the threat of multidrug-resistant bacterial pathogens. Recently, deep generative models including generative adversarial networks (GANs) have been shown to be capable of designing new antimicrobial ...
Duy Phuoc, Tran, Yoshihiro, Ito, Koji, Tsuda, Andrejs, Tucs, Takanori, Uzawa, Akiko, Yumoto   +5 more
core   +1 more source

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +1 more source

Study of image reconstruction efficiency in a single-pixel imaging method using generative adversarial networks

Компьютерная оптика
Single-pixel imaging is a promising image acquisition method that provides an alternative to traditional imaging methods using multi-pixel matrices. However, algorithmic image reconstruction from measurements of a single-pixel camera is a non-trivial ...
D.V. Babukhin, A.A. Reutov, D.V. Sych
doaj   +1 more source

Attribute-Aware Generative Design With Generative Adversarial Networks

IEEE Access, 2020
The designers' tendency to adhere to a specific mental set and heavy emotional investment in their initial ideas often limit their ability to innovate during the design ideation process.
Chenxi Yuan, Mohsen Moghaddam
doaj   +1 more source

Inverse Design of Amorphous Materials With Targeted Properties

Advanced Materials, EarlyView.
AMDEN is a diffusion model framework for the inverse design of amorphous materials with targeted properties. By incorporating Hamiltonian Monte Carlo refinement into the denoising process, the framework overcomes the challenge of generating thermally relaxed disordered structures.
Jonas A. Finkler, Yan Lin, Tao Du, Jilin Hu, Morten M. Smedskjaer   +4 more
wiley   +1 more source