Results 141 to 150 of about 39,370 (305)

Deep Convolutional Generative Adversarial Networks in Image-Based Android Malware Detection

Computers
The recent advancements in generative adversarial networks have showcased their remarkable ability to create images that are indistinguishable from real ones.
Francesco Mercaldo, Fabio Martinelli, Antonella Santone   +2 more
doaj   +1 more source

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, Zijian Huang, Xuanwu Wang, Zhenghui Feng, Wei Liu, Bingding Huang   +5 more
wiley   +1 more source

Generating Images Using Generative Adversarial Networks

, 2020
U ovom radu su objašnjene neuronske mreže, konvolucijske neuronske mreže i generativne suparničke mreže. Cilj je generiranje slika u boji korištenjem ACGAN-a.
Bartol, Mirko
core  

Object-Oriented Generative Adversarial Networks

, 2020
(Performed in 2018 as a class project) Deep learning is a field that has been mainly driven by connectionist models like neural networks, characterized by layered processing of distributed, sub-symbolic and statistical features. However, human high-level
Qianli Liao
core   +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

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

Adversarial scheduling analysis of Game-Theoretic Models of Norm Diffusion.

In (Istrate et al. SODA 2001) we advocated the investigation of robustness of results in the theory of learning in games under adversarial scheduling models.
Istrate, Gabriel, Marathe, Madhav V., Ravi, S.S.   +2 more
core  

Generative adversarial networks enhanced location privacy in 5G networks

, 2020
Generative adversarial networks enhanced location privacy in 5G ...
J Zhang (7490018), Y Qu (9976652), X Zhang (6084905), S Yu (7291433), X Zhai (7687730), R Li (660811)   +5 more
core  

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

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