Applications of GANs | Generative Adversarial Network

This category encompasses a range of advanced applications of generative adversarial networks (GANs) across various fields. Key themes include enhancements in image synthesis, malware detection, anomaly detection, and the integration of GANs with other machine learning models. The documents highlight innovative approaches for transforming data, such as converting sketches to realistic images, improving cybersecurity, and enhancing interpretability in AI systems. Significant applications span law enforcement, mobile security, and public health, demonstrating the versatility and impact of GAN technology.

Factorizing Transforms Computer Vision.pptx
Deep learning-based image super-resolution using generative adversarial networks with adaptive loss functions
Asurvey of missing data imputation techniques: statistical methods, machine learning models, and GAN-based approaches
Generative Adversarial Networks inn Deep learning
Utilization of convolutional neural network in image interpretation techniques for detecting kidney disease
Using Gen AI Agents With GAE and VAE to Enhance Resilience of US Markets
CryptoGAN: a new frontier in generative adversarial network driven image encryption
Artificial Intelligence (AI), Robotics, Computational Fluid Dynamics
How Apple Uses Artificial Intelligence to Redefine Innovation
Malware Detection and Classification using Generative Adversarial Network
AI Applications in Education - Current and Future Trends - Updated December 2024.pdf
Face mask classification using convolutional neural networks with facial image regions and super resolution
Generative adversarial network-based phishing URL detection with variational autoencoder and transformer
Machine learning-based anomaly detection for smart home networks under adversarial attack
introduction to deep learning the components .pptx
What is a GAN Generative Adversarial Networks Guide.pdf
An unsupervised generative adversarial network based-host intrusion detection system for internet of things devices
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The Challenge of Interpretability in Generative AI Models.pdf
Optimal text-to-image synthesis model for generating portrait images using generative adversarial network techniques