Results 131 to 140 of about 4,829,196 (302)
Generative Adversarial Networks in Speech Enhancement: A Survey
IEEE AccessGenerative adversarial networks are a powerful type of model in deep learning. They have been successfully applied within different domains. This review focuses on the usage of generative adversarial networks for speech enhancement.
Justina Ramonaite +2 more
doaj +1 more source
Font Creation Using Class Discriminative Deep Convolutional Generative Adversarial Networks
, 2017 In this research, we attempt to generate fonts automatically using a modification of a Deep Convolutional Generative Adversarial Network (DCGAN) by introducing class consideration. DCGANs are the application of generative adversarial networks (GAN) which
Seiichi Uchida +7 more
core +1 more source
Cell Segmentation Beyond 2D—A Review of the State‐of‐the‐Art
Advanced Intelligent Discovery, EarlyView.Cell segmentation underpins many biological image analysis tasks, yet most deep learning methods remain limited to 2D despite the inherently 3D nature of cellular processes. This review surveys segmentation approaches beyond 2D, comparing 2.5D and fully 3D methods, analyzing 31 models and 32 volumetric datasets, and introducing a unified reference ...
Fabian Schmeisser +6 more
wiley +1 more source
Generative Adversarial Network for Videos and Saliency Map [PDF]
, 2018 Faculty advisor: Qi ZhaoThis research was supported by the Undergraduate Research Opportunities Program (UROP).Batsaikhan, Bat-Orgil. (2018). Generative Adversarial Network for Videos and Saliency Map.
Batsaikhan, Bat-Orgil
core
Composition‐Aware Cross‐Sectional Integration for Spatial Transcriptomics
Advanced Intelligent Discovery, EarlyView.Multi‐section spatial transcriptomics demands coherent cell‐type deconvolution, domain detection, and batch correction, yet existing pipelines treat these tasks separately. FUSION unifies them within a composition‐aware latent framework, modeling reads as cell‐type–specific topics and clustering in embedding space.
Qishi Dong +5 more
wiley +1 more source
Graphical Generative Adversarial Networks
CoRR, 2018 We propose Graphical Generative Adversarial Networks (Graphical-GAN) to model structured data. Graphical-GAN conjoins the power of Bayesian networks on compactly representing the dependency structures among random variables and that of generative adversarial networks on learning expressive dependency functions.
Chongxuan Li +3 more
openaire +3 more sources
Harnessing Machine Learning to Understand and Design Disordered Solids
Advanced Intelligent Discovery, EarlyView.This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source
Predicting Novel Views Using Generative Adversarial Query Network [PDF]
, 2019 The problem of predicting a novel view of the scene using an arbitrary number of observations is a challenging problem for computers as well as for humans.
Rahtu, Esa +7 more
core +1 more source
AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective
Advanced Intelligent Discovery, EarlyView.The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley +1 more source