Results 81 to 90 of about 2,911,065 (235)
What are GANs?: Introducing Generative Adversarial Networks to Middle School Students
, 2021 Applications of Generative Machine Learning techniques such as Generative Adversarial Networks (GANs) are used to generate new instances of images, music, text, and videos.
Breazeal, Cynthia +2 more
core +1 more source
Advanced Intelligent Discovery, EarlyView.Sequential multicolor fluorescence imaging in dynamic microsystems is constrained by acquisition speed and excitation dose. This study introduces a real‐time framework to reconstruct spectrally separated channels from reduced cross‐channel acquisitions (frames containing mixed spectral contributions).
Juan J. Huaroto +3 more
wiley +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
Compression artifacts reduction by improved generative adversarial networks
EURASIP Journal on Image and Video Processing, 2019 In this paper, we propose an improved generative adversarial network (GAN) for image compression artifacts reduction task (artifacts reduction by GANs, ARGAN).
Zengshun Zhao +5 more
doaj +1 more source
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 Understanding human motion behavior is critical for autonomous moving platforms (like self-driving cars and social robots) if they are to navigate human-centric environments. This is challenging because human motion is inherently multimodal: given a history of human motion paths, there are many socially plausible ways that people could move in the ...
Gupta, Agrim +4 more
openaire +3 more sources
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
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
Applied Sciences, 2018 Recently, many studies have reported on image synthesis based on Generative Adversarial Networks (GAN). However, the use of GAN does not provide much attention on the signal classification problem.
Caidan Zhao +3 more
doaj +1 more source
IVE-GAN: Invariant Encoding Generative Adversarial Networks
CoRR, 2017 under review at ...
Robin Winter, Djork-Arné Clevert
openaire +2 more sources
Generative Adversarial Networks and Other Generative Models
, 2022 Generative networks are fundamentally different in their aim and methods compared to CNNs for classification, segmentation, or object detection. They have initially not been meant to be an image analysis tool, but to produce naturally looking images. The
Wenzel, Markus
core