Results 41 to 50 of about 3,042,897 (294)

Variational Generative Adversarial Networks for Preventing Mode Collapse [PDF]

هوش محاسباتی در مهندسی برق, 2022
Generative models try to obtain a probability distribution that is similar to that of observed data. Two different solutions have been proposed in this regard in recent years: one is to minimize the divergence (distance) between the two distributions by ...
Mehdi Jamaseb Khollari, Vali Derhami, Mehdi Yazdian Dehkordi   +2 more
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

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

Structured Generative Adversarial Networks

CoRR, 2017
We study the problem of conditional generative modeling based on designated semantics or structures. Existing models that build conditional generators either require massive labeled instances as supervision or are unable to accurately control the semantics of generated samples.
Zhijie Deng, Hao Zhang 0025, Xiaodan Liang, Luona Yang, Shizhen Xu, Jun Zhu 0001, Eric P. Xing   +6 more
openaire   +4 more sources

Regularized Generative Adversarial Network [PDF]

SSRN Electronic Journal, 2021
18 pages. Comments are welcome!
Gabriele Di Cerbo, Ali Hirsa, Ahmad Shayaan   +2 more
openaire   +3 more sources

Cell Morphology-Guided De Novo Hit Design by Conditioning Generative Adversarial Networks on Phenotypic Image Features [PDF]

, 2020
Developing new small molecules that are bioactive is time-consuming, costly and rarely successful. As a mitigation strategy, we apply, for the first time, generative adversarial networks to de novo design of small molecules using a phenotype-based drug ...
David, Rouquié, Djork-Arné, Clevert, Paula Andrea Marin, Zapata, Oscar, Méndez-Lucio, Joerg, Wichard   +4 more
core   +1 more source

Slimmable Generative Adversarial Networks

Proceedings of the AAAI Conference on Artificial Intelligence, 2021
Generative adversarial networks (GANs) have achieved remarkable progress in recent years, but the continuously growing scale of models make them challenging to deploy widely in practical applications. In particular, for real-time generation tasks, different devices require generators of different sizes due to varying computing power.
Liang Hou, Zehuan Yuan, Lei Huang 0015, Huawei Shen, Xueqi Cheng, Changhu Wang   +5 more
openaire   +4 more sources

Geological facies modeling based on Generative Adversarial Networks (GANs)

, 2020
Geological facies modeling based on progressive growing of Generative Adversarial Networks ...
Suihong Song
core   +2 more sources

Generative Adversarial Mapping Networks

CoRR, 2017
9 pages, 7 ...
Jianbo Guo, Guangxiang Zhu, Jian Li
openaire   +3 more sources

Generative Adversarial Optical Networks Using Diffractive Layers for Digit and Action Generation

Photonics
Within the traditional electronic neural network framework, Generative Adversarial Networks (GANs) have achieved extensive applications across multiple domains, including image synthesis, style transfer and data augmentation.
Pei Hu, Tengyu Cui, Yuanyuan Zhang, Shuai Feng   +3 more
doaj   +1 more source