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Generative Adversarial Network

Papers

Showing 676700 of 3110 papers

TitleStatusHype
Curvature Graph Generative Adversarial NetworksCode0
Adaptive Gradient Balancing for UndersampledMRI Reconstruction and Image-to-Image TranslationCode0
CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 DetectionCode0
GANISP: a GAN-assisted Importance SPlitting Probability EstimatorCode0
CVEGAN: A Perceptually-inspired GAN for Compressed Video EnhancementCode0
AdaGAN: Adaptive GAN for Many-to-Many Non-Parallel Voice ConversionCode0
Genome-AC-GAN: Enhancing Synthetic Genotype Generation through Auxiliary ClassificationCode0
GANkyoku: a Generative Adversarial Network for Shakuhachi MusicCode0
GANomaly: Semi-Supervised Anomaly Detection via Adversarial TrainingCode0
GAN-QP: A Novel GAN Framework without Gradient Vanishing and Lipschitz ConstraintCode0
Injecting and removing malignant features in mammography with CycleGAN: Investigation of an automated adversarial attack using neural networksCode0
FSRNet: End-to-End Learning Face Super-Resolution with Facial PriorsCode0
Coupled Generative Adversarial NetworksCode0
A Bayesian Data Augmentation Approach for Learning Deep ModelsCode0
DADA: Deep Adversarial Data Augmentation for Extremely Low Data Regime ClassificationCode0
From W-Net to CDGAN: Bi-temporal Change Detection via Deep Learning TechniquesCode0
CDMA: A Practical Cross-Device Federated Learning Algorithm for General Minimax ProblemsCode0
Rob-GAN: Generator, Discriminator, and Adversarial AttackerCode0
Free-form tumor synthesis in computed tomography images via richer generative adversarial networkCode0
Flow-GAN: Combining Maximum Likelihood and Adversarial Learning in Generative ModelsCode0
FLAGNet : Feature Label based Automatic Generation Network for symbolic musicCode0
HDL: Hybrid Deep Learning for the Synthesis of Myocardial Velocity Maps in Digital Twins for Cardiac AnalysisCode0
HexaGAN: Generative Adversarial Nets for Real World ClassificationCode0
From Third Person to First Person: Dataset and Baselines for Synthesis and RetrievalCode0
Co-supervised learning paradigm with conditional generative adversarial networks for sample-efficient classificationCode0
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