Results 101 to 110 of about 4,829,196 (302)

Architecture of the generator and the three discriminators used in our Generative Multi Adversarial Network.

, 2019
Architecture of the generator and the three discriminators used in our Generative Multi Adversarial Network.
Bir Bhanu (2154277), Rajkumar Theagarajan (6429590)   +1 more
core   +1 more source

A PRIMER ON GENERATIVE ADVERSARIAL NETWORKS

International Journal of Innovative Research in Computer Science & Technology, 2020
Generative Adversarial Networks (GANs) is a type of deep neural network architecture that utilizes unsupervised machine learning to generate data. They were presented in 2014, in a paper by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. This paper will introduce the core components of GANs.
Dr. Vikas Thada, Mr. Utpal Shrivastava, Jyotsna Sharma, Kuwar Prateek Singh, Manda Ranadeep   +4 more
openaire   +1 more source

Structural Divergence Between the Moltbook AI‐Agent Network and Human Social Networks

Advanced Science, EarlyView.
Analysis of the Moltbook AI‐agent network reveals a striking combination of familiar global scaling and distinct internal organization. Attention is highly concentrated, reciprocity is limited, connected triads are suppressed, and communities are strongly modular.
Wenpin Hou, Zhicheng Ji
wiley   +1 more source

Few-shot Hyperspectral Image Classification using Relational Generative Adversarial Network

Hyperspectral image (HSI) classification is an essential task in remote sensing, but its performance is greatly affected by limited labeled samples.
Guo, Baoqing, Xiao, Chuangbai, Zhou, Jun, Wei, Wei, Tong, Lei   +4 more
core   +1 more source

Conditional Wasserstein Generative Adversarial Network 를 활용한 메타표면 설계

, 2022
MasterAs a method for inverse design of metasurfaces, we propose a conditional Wasserstein generative adversarial network that combines the design idea of a conditional generative adversarial network with the optimization method of a Wasserstein ...
김선욱
core  

Coevolution of Generative Adversarial Networks [PDF]

, 2019
Published in EvoApplications ...
Victor Costa, Nuno Lourenço 0002, Penousal Machado   +2 more
openaire   +3 more sources

Molecular Atlas of Key Food Odorants Reveals Mixture‐Level Organization and Enables Generative Aroma Design

Advanced Science, EarlyView.
KFO‐Atlas reveals how real‐world aromas are organized as structured mixtures rather than individual molecules. Building on these principles, KFO‐Gen, a generative AI framework, designs perceptually valid aroma formulations and reconstructs meat‐like aromas exclusively from plant‐derived odorants, providing a foundation for mixture‐level studies and AI ...
Jingzhi Zhang, Huadong Xing, Antonella Di Pizio, Qinfei Ke, Xingran Kou, Dachuan Zhang   +5 more
wiley   +1 more source

Epistemic Generative Adversarial Networks

CoRR
Generative models, particularly Generative Adversarial Networks (GANs), often suffer from a lack of output diversity, frequently generating similar samples rather than a wide range of variations. This paper introduces a novel generalization of the GAN loss function based on Dempster-Shafer theory of evidence, applied to both the generator and ...
Muhammad Mubashar, Fabio Cuzzolin
openaire   +2 more sources

Annealed Generative Adversarial Networks

CoRR, 2017
9 pages, 6 ...
Arash Mehrjou, Bernhard Schölkopf, Saeed Saremi   +2 more
openaire   +2 more sources

scGEN: Leveraging an Adaptive Weighting Strategy for Challenging Cell Clustering in Single‐Cell RNA‐seq

Advanced Science, EarlyView.
scGEN combines attribute–structure encoding, adaptive hard‐sample weighting, and regularized zero‐inflated negative binomial loss to resolve ambiguous single‐cell states. In the developing human pituitary, refined annotations reveal Pre.Gonado cells at 7 weeks and previously obscured developmental communication networks.
Fang Huang, Dayu Hu, Yaqi Dai, Aoyi Geng, Renxiang Guan, Hao Quan, Brett A. McGregor, Zhihan Wang, Nadeem Khan, Hongsheng Zhang, Jinghua Zhang, Mingyang Xia, Xiaoqiong Gu, Di Che, Junguk Hur, Xiaoyu Cui, Kai Guo   +16 more
wiley   +1 more source