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MNIST and celebA
MNIST and celebA datasets were used to train and evaluate the proposed QDCGAN architecture. -
Automated Seed Quality Testing System using GAN & Active Learning
A dataset of 26K corn seed images, classified into pure seeds and three other defective classes (broken, silkcut, and discolored). -
Structurally aware bidirectional unpaired image to image translation between ...
Magnetic Resonance (MR) Imaging and Computed Tomography (CT) are the primary diagnostic imaging modalities quite frequently used for surgical planning and analysis. A general... -
GAN Training Dataset
The dataset used for training the generative adversarial network (GAN) model. -
GAN-EM: GAN based EM Learning Framework
GAN-EM: A GAN based EM Learning Framework for image clustering, semi-supervised classification and dimensionality reduction. -
Mixture of 25 2D-Gaussian Distributions
The dataset used in the Discriminator Rejection Sampling (DRS) algorithm, consisting of a mixture of 25 2D-Gaussian distributions. -
GAN Training Dynamics
The dataset used in this paper is a GAN training dataset, which is a non-convex game. -
This-city-does-not-exist Dataset
The dataset is used to test the generalization capabilities of the VQ-VAE 2 one-class classifier. -
Alps Dataset
The dataset is used to train and test the VQ-VAE 2 one-class classifier for detecting GAN manipulated multispectral satellite images. -
China and Scandinavian Season Transfer Datasets
The dataset is used to train and test the VQ-VAE 2 one-class classifier for detecting GAN manipulated multispectral satellite images. -
Land Cover (LC) Transfer Datasets
The dataset is used to train and test the VQ-VAE 2 one-class classifier for detecting GAN manipulated multispectral satellite images. -
Database in [28]
The database in [28] which was used to evaluate SEGAN in [14]. -
BiHMP-GAN: Bidirectional 3D Human Motion Prediction GAN
Human motion prediction model has applications in various fields of computer vision. Without taking into account the inherent stochasticity in the prediction of future pose... -
Dual-Attention GAN for Large-Pose Face Frontalization
Face frontalization provides an effective and efficient way for face data augmentation and further improves the face recognition performance in extreme pose scenario. -
Generative Adversarial Active Learning
Generative Adversarial Active Learning (GAAL) algorithm using Generative Adversarial Networks (GAN) for active learning by query synthesis. -
One-Shot GAN Generated Fake Face Detection
This paper proposes a universal One-Shot GAN generated fake face detection method which can be used in significantly different areas of anomaly detection. -
Generative Adversarial Networks
Generative Adversarial Networks (GANs) consist of two networks: a generator G(z) and a discriminator D(x). The discriminator is trying to distinguish real objects from objects... -
Unlabeled samples generated by GAN improve the person re-identification basel...
A dataset for unsupervised person re-identification using Generative Adversarial Networks (GANs). -
Synthetic dataset for hyperbolic GAN
The dataset used in the paper is a synthetic dataset generated using the proposed hyperbolic generative adversarial network (GAN) model.
