• Aucun résultat trouvé

Generative Adversarial Networks GANs

A RESIDUAL DENSE GENERATIVE ADVERSARIAL NETWORK FOR PANSHARPENING WITH GEOMETRICAL CONSTRAINTS

A RESIDUAL DENSE GENERATIVE ADVERSARIAL NETWORK FOR PANSHARPENING WITH GEOMETRICAL CONSTRAINTS

... of GANs for the ge- nerative problem. Generative Adversarial Networks (GANs) are a class of unsupervised learning algorithms introduced by Goodfellow et ...a generative mo- del ...

6

An Adversarial Regularisation for Semi-Supervised Training of Structured Output Neural Networks

An Adversarial Regularisation for Semi-Supervised Training of Structured Output Neural Networks

... the Generative Adversarial Networks (GANs) [ 5 ...of GANs is that all that is required for training the generator is a collection of samples from the target ...of GANs to ...

8

Data Augmentation by Generative Adversarial Networks for Semantic Segmentation of Satellite Images

Data Augmentation by Generative Adversarial Networks for Semantic Segmentation of Satellite Images

... • We propose to use Generative Adversarial Networks (GANs) for better data augmentation by performing style transfer between images. • Our proposed learning method consists of a data augmentor ...

2

LIGHT FIELD IMAGE CODING USING DUAL DISCRIMINATOR GENERATIVE ADVERSARIAL NETWORK AND VVC TEMPORAL SCALABILITY

LIGHT FIELD IMAGE CODING USING DUAL DISCRIMINATOR GENERATIVE ADVERSARIAL NETWORK AND VVC TEMPORAL SCALABILITY

... Discriminator Generative Adversarial Nets Generative Adversarial Networks (GANs) are deep neural net architectures composed of two consecutive neural network models, namely ...

7

Simultaneous super-resolution and segmentation using a generative adversarial network: Application to neonatal brain MRI

Simultaneous super-resolution and segmentation using a generative adversarial network: Application to neonatal brain MRI

... neural networks (CNNs) yields promising results for MRI data [2, ...5]. Generative adversarial networks (GANs) have thus been proposed to estimate textured and sharper images [5, ...

5

Comparing Representations for Audio Synthesis Using Generative Adversarial Networks

Comparing Representations for Audio Synthesis Using Generative Adversarial Networks

... LTCI, T´elecom Paris Institut Polytechnique de Paris, France Abstract—In this paper, we compare different audio signal representations, including the raw audio waveform and a variety of time-frequency representations, ...

6

ColorMapGAN: Unsupervised Domain Adaptation for Semantic Segmentation Using Color Mapping Generative Adversarial Networks

ColorMapGAN: Unsupervised Domain Adaptation for Semantic Segmentation Using Color Mapping Generative Adversarial Networks

... Mapping Generative Adversarial Networks (ColorMapGAN), which can generate fake training images that are semantically exactly the same as training images, but whose spectral distribution is similar to ...

17

Generative models for natural images

Generative models for natural images

... Abstract Generative adversarial networks (GANs) are powerful generative models, but suffer from training ...of GANs, but can still generate low-quality samples or fail to ...

88

Designing complex architectured materials with generative adversarial networks

Designing complex architectured materials with generative adversarial networks

... the generative adversarial networks (GANs; ...the GANs are capable of promptly generating new configurations that approximately achieve the extreme properties ...

9

Multi-view Generative Adversarial Networks

Multi-view Generative Adversarial Networks

... Bidirectional Generative Adversarial Nets (BiGAN) We quickly remind the principle of BiGANs since our model is an extension of this ...technique. Generative Adversarial Networks (GAN) ...

13

Biconditional Generative Adversarial Networks for Multiview Learning with Missing Views

Biconditional Generative Adversarial Networks for Multiview Learning with Missing Views

... 2 GANs for multiview multiclass classification where observations may have missing ...neural- networks implementing a three players game between a discriminator and two ...2 GANs to generate high ...

16

Dynamic Facial Expression Generation on Hilbert Hypersphere with Conditional Wasserstein Generative Adversarial Nets

Dynamic Facial Expression Generation on Hilbert Hypersphere with Conditional Wasserstein Generative Adversarial Nets

... Generative Adversarial Networks – Recently, GANs have shown to be extremely efficient for synthesizing realis- tic ...images. GANs have been used for several applications in- cluding ...

22

Semantic Segmentation using Adversarial Networks

Semantic Segmentation using Adversarial Networks

... an adversarial loss ...the adversarial net has access to large portions or the entire output image, it can be interpreted as a learned higher-order loss, which obviates the need to manually design ...

13

Generative Adversarial Networks for Realistic Synthesis of Hyperspectral Samples

Generative Adversarial Networks for Realistic Synthesis of Hyperspectral Samples

... Finally, as GANs map a latent noise space to the signal space, it is possible to explore the spectral manifold by in- terpolating between two noise vectors. Within a fixed class, it allows to generate spectra ...

5

Generative models : a critical review

Generative models : a critical review

... While generative adversarial networks have been a driving force in the relatively rapid improvement in the quality of image generation models, there are ways in which VAEs are ...of GANs is ...

102

On the Relationships between Generative Encodings, Regularity, and Learning Abilities when Evolving Plastic Artificial Neural Networks

On the Relationships between Generative Encodings, Regularity, and Learning Abilities when Evolving Plastic Artificial Neural Networks

... neural networks that can learn in a large variety of ...generic networks that apply the same learning rules to whole sets of inputs instead of networks that are finely- tuned to only solve the test ...

13

Adversarial Robustness via Label-Smoothing

Adversarial Robustness via Label-Smoothing

... improving adversarial robustness of super- vised deep-learning ...method: adversarial, Boltzmann and second- best Label-Smoothing methods, and we ex- plain how to construct your own ...improves ...

13

Brain networks of rats under anesthesia using resting-state fMRI: comparison with dead rats, random noise and generative models of networks

Brain networks of rats under anesthesia using resting-state fMRI: comparison with dead rats, random noise and generative models of networks

... networks, the degree distribution leads to nodes with high values. In many fMRI studies, the objective is to evaluate the connectivity between different brain regions in order to characterize their role in a ...

30

Adversarial Games for Particle Physics

Adversarial Games for Particle Physics

... • The generation process is often not uniquely specified or known exactly, hence systematic uncertainties.. • Parametrization through.[r] ...

33

Adversarial Games for Particle Physics

Adversarial Games for Particle Physics

... • Adversarial training = indirectly specifying complicated loss functions. For generation[r] ...

40

Show all 3989 documents...

Sujets connexes