Results 61 to 70 of about 219,974 (297)

Generative Adversarial Networks: An Overview [PDF]

IEEE Signal Processing Magazine, 2018
Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this through deriving backpropagation signals through a competitive process involving a pair of networks. The representations that can be learned by GANs may be used in a variety of applications, including image ...
Antonia Creswell, Tom White, Vincent Dumoulin, Kai Arulkumaran, Biswa Sengupta, Anil A. Bharath   +5 more
openaire   +4 more sources

Generative Adversarial Networks: A Primer for Radiologists

RadioGraphics, 2021
Artificial intelligence techniques involving the use of artificial neural networks-that is, deep learning techniques-are expected to have a major effect on radiology. Some of the most exciting applications of deep learning in radiology make use of generative adversarial networks (GANs).
Jelmer M. Wolterink, Anirban Mukhopadhyay, Tim Leiner, Thomas J. Vogl, Andreas M. Bucher, Ivana Išgum   +5 more
openaire   +5 more sources

Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative Models

, 2018
Adversarial learning of probabilistic models has recently emerged as a promising alternative to maximum likelihood. Implicit models such as generative adversarial networks (GAN) often generate better samples compared to explicit models trained by maximum
Dhar, Manik, Ermon, Stefano, Grover, Aditya   +2 more
core   +1 more source

Meniscus Pixel Printing for Contact‐Lens Vision Sensing and Robotic Control

Advanced Functional Materials, EarlyView.
A visual‐sensing contact lens is enabled by meniscus pixel printing (MPP), which rapidly patterns a 200 µm perovskite photodetector pixel in 1 s without masks, vacuum processing, or bulky equipment. A deep‐learning‐based super‐resolution reconstructs sparse on‐lens signals into 80 × 80 high‐resolution visual information, while AI‐driven eye‐tracking ...
Byung‐Hoon Gong, Dohyean Kim, Jiyun Jeong, Taekyeong Kim, Soon‐sung Kwon, Junyoung Seo, Il Jeon, Im Doo Jung   +7 more
wiley   +1 more source

Attentively Conditioned Generative Adversarial Network for Semantic Segmentation

IEEE Access, 2020
Generative Adversarial Network has proven to produce state-of-the-art results by framing a generative modeling task into a supervised learning problem. In this paper, we propose Attentively Conditioned Generative Adversarial Network (ACGAN) for semantic ...
Ariyo Oluwasanmi, Muhammad Umar Aftab, Akeem Shokanbi, Jehoiada Jackson, Bulbula Kumeda, Zhiquang Qin   +5 more
doaj   +1 more source

Aircraft Trajectory Prediction Enhanced through Resilient Generative Adversarial Networks Secured by Blockchain: Application to UAS-S4 Ehécatl

Applied Sciences, 2023
This paper introduces a novel and robust data-driven algorithm designed for Aircraft Trajectory Prediction (ATP). The approach employs a Neural Network architecture to predict future aircraft trajectories, utilizing input variables such as latitude ...
Seyed Mohammad Hashemi, Seyed Ali Hashemi, Ruxandra Mihaela Botez, Georges Ghazi   +3 more
doaj   +1 more source

Computational Modeling Meets 3D Bioprinting: Emerging Synergies in Cardiovascular Disease Modeling

Advanced Healthcare Materials, EarlyView.
Emerging advances in three‐dimensional bioprinting and computational modeling are reshaping cardiovascular (CV) research by enabling more realistic, patient‐specific tissue platforms. This review surveys cutting‐edge approaches that merge biomimetic CV constructs with computational simulations to overcome the limitations of traditional models, improve ...
Tanmay Mukherjee, Mehdi Salar Amoli, Sarah Rezapourdamanab, Lama Rita El Shammas, Martin L. Tomov, Emilio A. Mendiola, Vahid Serpooshan, Reza Avazmohammadi   +7 more
wiley   +1 more source

Adversarial Variational Optimization of Non-Differentiable Simulators [PDF]

, 2019
Complex computer simulators are increasingly used across fields of science as generative models tying parameters of an underlying theory to experimental observations.
Cranmer, Kyle, Hermans, Joeri, Louppe, Gilles   +2 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

StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation [PDF]

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2017
Recent studies have shown remarkable success in image-to-image translation for two domains. However, existing approaches have limited scalability and robustness in handling more than two domains, since different models should be built independently for ...
Yunjey Choi, Min-Je Choi, M. Kim, Jung-Woo Ha, Sunghun Kim, J. Choo   +5 more
semanticscholar   +1 more source