Results 11 to 20 of about 16,046 (295)

Dynamics of Fourier Modes in Torus Generative Adversarial Networks

Mathematics, 2021
Generative Adversarial Networks (GANs) are powerful machine learning models capable of generating fully synthetic samples of a desired phenomenon with a high resolution.
Ángel González-Prieto, Alberto Mozo, Edgar Talavera, Sandra Gómez-Canaval   +3 more
doaj   +2 more sources

Games of GANs: Game-Theoretical Models for Generative Adversarial Networks

Artificial Intelligence Review, 2022
Abstract Generative Adversarial Networks (GANs) have recently attracted considerable attention in the AI community due to their ability to generate high-quality data of significant statistical resemblance to real data. Fundamentally, GAN is a game between two neural networks trained in an adversarial manner to reach a zero-sum Nash equilibrium ...
Monireh Mohebbi Moghaddam, Bahar Boroomand, Mohammad Jalali, Arman Zareian, Alireza Daeijavad, Mohammad Hossein Manshaei, Marwan Krunz   +6 more
openaire   +3 more sources

LDDMM Meets GANs: Generative Adversarial Networks for Diffeomorphic Registration

Jornada de Jóvenes Investigadores del I3A, 2021
In this work, we propose an unsupervised adversarial learning LDDMM method for 3D mono-modal images based on Generative Adversarial Networks. We have successfully implemented two models with stationary and EPDiff constrained non-stationary parameterizations of diffeomorphisms.
Ubaldo Ramon, Monica Hernandez, Elvira Mayordomo   +2 more
openaire   +5 more sources

Meta-learning Enabled Score-Based Generative Adversarial Networks (GANs)

International Journal of Computational Intelligence Systems
The capacity of Generative Adversarial Networks (GANs) to provide high-quality data has led to their significant attention. On the other hand, hyperparameter adjustment is a common part of GAN training, which may increase computing costs and result in ...
P. Navaneethakrishnan, Smitha Elsa Peter, Sishaj P. Simon, M. Irshad Ahamed   +3 more
doaj   +2 more sources

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

I-GANs for Infrared Image Generation

Complexity, 2021
The making of infrared templates is of great significance for improving the accuracy and precision of infrared imaging guidance. However, collecting infrared images from fields is difficult, of high cost, and time-consuming.
Bing Li, Yong Xian, Juan Su, Da Q. Zhang, Wei L. Guo   +4 more
doaj   +1 more source

Augmenting Generative Adversarial Networks for Speech Emotion Recognition [PDF]

, 2020
Generative adversarial networks (GANs) have shown potential in learning emotional attributes and generating new data samples. However, their performance is usually hindered by the unavailability of larger speech emotion recognition (SER) data.
Raja Jurdak, Björn W. Schuller, Siddique Latif, Latif, Siddique, Asim, Muhammad, Rana, Rajib, Sara Khalifa, Schuller, Björn W., Khalifa, Sara, Jurdak, Raja, Rajib Rana, Muhammad Asim   +11 more
core   +1 more source

Generative Networks and Royalty-Free Products

Sakarya University Journal of Computer and Information Sciences, 2020
In recent years, with the increasing power of computers and Graphics Processing Units (GPUs), vast variety of deep neural networks architectures have been created and realized.
Yasin Özkan, Pakize Erdoğmuş
doaj   +1 more source

Numerical Analysis of Bio-signal Using Generative Adversarial Networks [PDF]

, 2020
In this decade, it is not necessary to have technical knowledge for the investment since the automatic algorithms to sell/buy investment destination have been developed with artificial intelligence (AI).
Ono, Rentarou, Rentarou Ono, Masumi Takada, Koki Nakane, Takada, Hiroki, Hiroki Takada, Takada, Masumi, Shota Yamamoto, Nakane, Koki, Yamamoto, Shota   +9 more
core   +1 more source

Pun-GAN: Generative Adversarial Network for Pun Generation [PDF]

Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2019
En este trabajo, nos centramos en la tarea de generar una oración de juego de palabras dado un par de sentidos de la palabra. Un desafío importante para la generación de juegos de palabras es la falta de un corpus de juegos de palabras a gran escala para guiar el aprendizaje supervisado. Para remediar esto, proponemos una red generativa adversaria para
Fuli Luo, Shunyao Li, Pengcheng Yang, Lei Li, Baobao Chang, Zhifang Sui, Xu Sun   +6 more
openaire   +2 more sources