Results 61 to 70 of about 3,042,897 (294)
Ensembles of Generative Adversarial Networks
CoRR, 2016 accepted NIPS 2016 Workshop on Adversarial ...
Yaxing Wang +2 more
openaire +2 more sources
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
Компьютерная оптикаSingle-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
Wasserstein Generative Adversarial Networks for Realistic Traffic Sign Image Generation
, 2021 Wasserstein Generative Adversarial Networks for Realistic Traffic Sign Image ...
Yan-Ting Liu (17719110) +2 more
core
Optimizing distributions over molecular space. An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry (ORGANIC) [PDF]
, 2017 Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over ...
Gabriel L., Guimaraes +3 more
core +1 more source
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 +4 more
openaire +3 more sources
Advanced Materials, EarlyView.Self‐invalidating multilayer physical unclonable functions (PUFs) integrate fingerprint‐like metal nanopattern bilayers from block copolymer templates with highly reactive reduced graphene oxide (rGO) interlayers. This tamper‐responsive architecture blocks all unauthorized physical and chemical replication attempts while autonomously deactivating ...
Gyu Hui Jo +7 more
wiley +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
LDDMM meets GANs: generative adversarial networks for diffeomorphic registration
, 2022 The purpose of this work is to contribute to the state of the art of deep-learning methods for diffeomorphic registration. We propose an adversarial learning LDDMM method for pairs of 3D mono-modal images based on Generative Adversarial Networks.
Ramón Júlvez, Ubaldo +2 more
core +1 more source