Papers Generative Adversarial Network
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Adversarial attacks to image classification systems using evolutionary algorithms
Image classification currently faces significant security challenges due to adversarial attacks, which consist of intentional alterations designed to deceive classification models based on artificial intelligence. This a…
ClassificationDiversityEvolutionary AlgorithmsGenerative Adversarial Network+2Generative Latent Kernel Modeling for Blind Motion Deblurring
Deep prior-based approaches have demonstrated remarkable success in blind motion deblurring (BMD) recently. These methods, however, are often limited by the high non-convexity of the underlying optimization process in BM…
DeblurringGenerative Adversarial NetworkSensitivityLightweight Safety Guardrails via Synthetic Data and RL-guided Adversarial Training
We introduce a lightweight yet highly effective safety guardrail framework for language models, demonstrating that small-scale language models can achieve, and even surpass, the performance of larger counterparts in cont…
Generative Adversarial NetworkSynthetic Data GenerationCAVGAN: Unifying Jailbreak and Defense of LLMs via Generative Adversarial Attacks on their Internal Representations
Security alignment enables the Large Language Model (LLM) to gain the protection against malicious queries, but various jailbreak attack methods reveal the vulnerability of this security mechanism. Previous studies have …
Generative Adversarial NetworkLarge Language ModelLLM JailbreakHybridQ: Hybrid Classical-Quantum Generative Adversarial Network for Skin Disease Image Generation
Machine learning-assisted diagnosis is gaining traction in skin disease detection, but training effective models requires large amounts of high-quality data. Skin disease datasets often suffer from class imbalance, priva…
Data AugmentationGenerative Adversarial NetworkImage GenerationGenerative Adversarial Evasion and Out-of-Distribution Detection for UAV Cyber-Attacks
The growing integration of UAVs into civilian airspace underscores the need for resilient and intelligent intrusion detection systems (IDS), as traditional anomaly detection methods often fail to identify novel threats. …
Anomaly DetectionGenerative Adversarial NetworkIntrusion DetectionOut-of-Distribution DetectionTime-series surrogates from energy consumers generated by machine learning approaches for long-term forecasting scenarios
Forecasting attracts a lot of research attention in the electricity value chain. However, most studies concentrate on short-term forecasting of generation or consumption with a focus on systems and less on individual con…
DenoisingGenerative Adversarial NetworkTime SeriesEfficient Beam Selection for ISAC in Cell-Free Massive MIMO via Digital Twin-Assisted Deep Reinforcement Learning
Beamforming enhances signal strength and quality by focusing energy in specific directions. This capability is particularly crucial in cell-free integrated sensing and communication (ISAC) systems, where multiple distrib…
Deep Reinforcement LearningGenerative Adversarial NetworkIntegrated sensing and communicationISACAn Attack Method for Medical Insurance Claim Fraud Detection based on Generative Adversarial Network
Insurance fraud detection represents a pivotal advancement in modern insurance service, providing intelligent and digitalized monitoring to enhance management and prevent fraud. It is crucial for ensuring the security an…
Fraud DetectionGenerative Adversarial NetworkPattern-Based Phase-Separation of Tracer and Dispersed Phase Particles in Two-Phase Defocusing Particle Tracking Velocimetry
This work investigates the feasibility of a post-processing-based approach for phase separation in defocusing particle tracking velocimetry for dispersed two-phase flows. The method enables the simultaneous 3D localizati…
Generative Adversarial NetworkPix2Geomodel: A Next-Generation Reservoir Geomodeling with Property-to-Property Translation
Accurate geological modeling is critical for reservoir characterization, yet traditional methods struggle with complex subsurface heterogeneity, and they have problems with conditioning to observed data. This study intro…
Generative Adversarial NetworkProperty PredictionMTSIC: Multi-stage Transformer-based GAN for Spectral Infrared Image Colorization
Thermal infrared (TIR) images, acquired through thermal radiation imaging, are unaffected by variations in lighting conditions and atmospheric haze. However, TIR images inherently lack color and texture information, limi…
ColorizationGenerative Adversarial NetworkImage ColorizationProportional Sensitivity in Generative Adversarial Network (GAN)-Augmented Brain Tumor Classification Using Convolutional Neural Network
Generative Adversarial Networks (GAN) have shown potential in expanding limited medical imaging datasets. This study explores how different ratios of GAN-generated and real brain tumor MRI images impact the performance o…
Brain Tumor ClassificationGenerative Adversarial NetworkSensitivityCWGAN-GP Augmented CAE for Jamming Detection in 5G-NR in Non-IID Datasets
In the ever-expanding domain of 5G-NR wireless cellular networks, over-the-air jamming attacks are prevalent as security attacks, compromising the quality of the received signal. We simulate a jamming environment by inco…
DenoisingGenerative Adversarial NetworkGenerative Algorithms for Wildfire Progression Reconstruction from Multi-Modal Satellite Active Fire Measurements and Terrain Height
Increasing wildfire occurrence has spurred growing interest in wildfire spread prediction. However, even the most complex wildfire models diverge from observed progression during multi-day simulations, motivating need fo…
Generative Adversarial NetworkA Privacy-Preserving Federated Learning Framework for Generalizable CBCT to Synthetic CT Translation in Head and Neck
Shortened Abstract Cone-beam computed tomography (CBCT) has become a widely adopted modality for image-guided radiotherapy (IGRT). However, CBCT suffers from increased noise, limited soft-tissue contrast, and artifacts, …
Federated LearningGenerative Adversarial NetworkPrivacy PreservingSSIMTabularQGAN: A Quantum Generative Model for Tabular Data
In this paper, we introduce a novel quantum generative model for synthesizing tabular data. Synthetic data is valuable in scenarios where real-world data is scarce or private, it can be used to augment or replace existin…
BenchmarkingGenerative Adversarial NetworkmodelReflectGAN: Modeling Vegetation Effects for Soil Carbon Estimation from Satellite Imagery
Soil organic carbon (SOC) is a critical indicator of soil health, but its accurate estimation from satellite imagery is hindered in vegetated regions due to spectral contamination from plant cover, which obscures soil re…
Generative Adversarial NetworkMemory-Efficient Super-Resolution of 3D Micro-CT Images Using Octree-Based GANs: Enhancing Resolution and Segmentation Accuracy
We present a memory-efficient algorithm for significantly enhancing the quality of segmented 3D micro-Computed Tomography (micro-CT) images of rocks using a generative model. The proposed model achieves a 16x increase in…
Generative Adversarial NetworkSuper-ResolutionUBGAN: Enhancing Coded Speech with Blind and Guided Bandwidth Extension
In practical application of speech codecs, a multitude of factors such as the quality of the radio connection, limiting hardware or required user experience necessitate trade-offs between achievable perceptual quality, e…
Bandwidth ExtensionGenerative Adversarial Network