Results 41 to 50 of about 221,929 (334)
Unsupervised Pixel-Level Domain Adaptation with Generative Adversarial Networks [PDF]
Computer Vision and Pattern Recognition, 2016 Collecting well-annotated image datasets to train modern machine learning algorithms is prohibitively expensive for many tasks. One appealing alternative is rendering synthetic data where ground-truth annotations are generated automatically ...
Konstantinos Bousmalis +4 more
semanticscholar +1 more source
Generative adversarial networks in EEG analysis: an overview
Journal of NeuroEngineering and Rehabilitation, 2023 Electroencephalogram (EEG) signals have been utilized in a variety of medical as well as engineering applications. However, one of the challenges associated with recording EEG data is the difficulty of recording large amounts of data.
Ahmed G. Habashi +3 more
semanticscholar +1 more source
StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2017 Although Generative Adversarial Networks (GANs) have shown remarkable success in various tasks, they still face challenges in generating high quality images.
Han Zhang +6 more
semanticscholar +1 more source
Dairy Goat Image Generation Based on Improved-Self-Attention Generative Adversarial Networks
IEEE Access, 2020 The lack of long-range dependence in convolutional neural networks causes weaker performance in generative adversarial networks(GANs) with regard to generating image details. The self-attention generative adversarial network(SAGAN) use the self-attention
Huan Li, Jinglei Tang
doaj +1 more source
Exploring generative adversarial networks and adversarial training
International Journal of Cognitive Computing in Engineering, 2022 Recognized as a realistic image generator, Generative Adversarial Network (GAN) occupies a progressive section in deep learning. Using generative modeling, the underlying generator model learns the real target distribution and outputs fake samples from ...
Afia Sajeeda, B M Mainul Hossain, Ph.D
doaj +1 more source
Semi-supervised Learning on Graphs Using Adversarial Training with Generated Sample [PDF]
Jisuanji kexue yu tansuo, 2023 Given a graph composed of a small number of labeled nodes and a large number of unlabeled nodes, semi-supervised learning on graphs aims to assign labels for the unlabeled nodes.
WANG Cong, WANG Jie, LIU Quanming, LIANG Jiye
doaj +1 more source
Learning Universal Adversarial Perturbations with Generative Models [PDF]
, 2018 Neural networks are known to be vulnerable to adversarial examples, inputs that have been intentionally perturbed to remain visually similar to the source input, but cause a misclassification.
Danezis, George, Hayes, Jamie
core +2 more sources
Quantum generative adversarial learning [PDF]
, 2018 Generative adversarial networks (GANs) represent a powerful tool for classical machine learning: a generator tries to create statistics for data that mimics those of a true data set, while a discriminator tries to discriminate between the true and fake ...
Lloyd, Seth, Weedbrook, Christian
core +2 more sources
DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-To-Image Synthesis [PDF]
Computer Vision and Pattern Recognition, 2019 In this paper, we focus on generating realistic images from text descriptions. Current methods first generate an initial image with rough shape and color, and then refine the initial image to a high-resolution one.
Minfeng Zhu +3 more
semanticscholar +1 more source
HGAN: Hybrid generative adversarial network [PDF]
Journal of Intelligent & Fuzzy Systems, 2021 In this paper, we present a simple approach to train Generative Adversarial Networks (GANs) in order to avoid a mode collapse issue. Implicit models such as GANs tend to generate better samples compared to explicit models that are trained on tractable data likelihood.
Iranmanesh, Seyed Mehdi +1 more
openaire +2 more sources