Generative Adversarial Networks
On the Use of Generative Adversarial Networks for Aircraft Trajectory Generation and Atypical Approach Detection
... CONCLUSION Generative adversarial networks have proven to be very effective in generating realistic scenes and objects in com- puter ...such generative machine learning ...
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SegSRGAN: Super-resolution and segmentation using generative adversarial networks — Application to neonatal brain MRI
... Keywords: super-resolution, segmentation, 3D generative adversarial networks, neonatal brain MRI, cortex. 1. Introduction Clinical studies on preterm newborns have shown that the majority of children ...
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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, ...
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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 ...
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Generative Adversarial Networks for Realistic Synthesis of Hyperspectral Samples
... neural networks. Especially, we investigate generative adversarial networks and their applica- tion to the synthesis of consistent labeled ...such networks on public datasets, we show ...
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Phase space sampling and operator confidence with generative adversarial networks
... trained generative adversarial net- work can be used to produce examples which are larger than those on which it was trained ...a generative adversarial network trained on a small system which ...
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Data Augmentation by Generative Adversarial Networks for Semantic Segmentation of Satellite Images
... [2] O. Tasar, Y. Tarabalka, and P. Alliez. Continual learning for dense labeling of satellite images. IEEE IGARSS, 2019. [3] O. Tasar, S L Happy, Y. Tarabalka, and P. Alliez. ColorMapGAN: Unsupervised domain adaptation ...
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Designing complex architectured materials with generative adversarial networks
... with generative adversarial networks. The networks are trained using simulation data from millions of randomly generated architectures categorized based on different crystallographic ...
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Multi-view Generative Adversarial Networks
... 2.3 General Idea We propose a model based on the Generative Adversarial Networks paradigm adapted to the multi- view prediction problem. Our model is based on two different principles: Conditional ...
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Biconditional Generative Adversarial Networks for Multiview Learning with Missing Views
... [10] Farquhar, J., Hardoon, D., Meng, H., Shawe-taylor, J.S., Szedmák, S.: Two view learning: Svm-2k, theory and practice. In: Advances in Neural Information Processing Systems 18, pp. 355–362 [11] Goodfellow, I., ...
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Generative adversarial networks as a novel approach for tectonic fault and fracture extraction in high resolution satellite and airborne optical images
... neural networks and the local feature extraction capability of image convolution, is called Deep Convolutional Network or ...a Generative Adversarial Network (GAN) ( Goodfellow et ...
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Interactive Example-Based Terrain Authoring with Conditional Generative Adversarial Networks
... D is crucial to the learning stage because its discriminative power conditions the quality of the generator G. Indeed, the generative network can only become e�cient in producing real examples if D is e�cient at ...
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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, ...
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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 generator G ...
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An Adversarial Regularisation for Semi-Supervised Training of Structured Output Neural Networks
... Our work is inspired by the Generative Adversarial Networks (GANs) [ 5 ]. In GAN, a generator network is trained to transform a random vector drawn from a simple sampling distribution to a sample ...
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Realistic synthesis of brain tumor resection ultrasound images with a generative adversarial network
... The simulation of realistic ultrasound (US) images has many applications in image-guided surgery such as image registration, data augmentation, or educational purposes. In this paper we simulated intraoperative US images ...
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From attribute-labels to faces: face generation using a conditional generative adversarial network
... Inria, Sophia Antipolis, France 2 Universit´ e Cˆ ote d’Azur, France {yaohui.wang, antitza.dantcheva, francois.bremond}@inria.fr Abstract. Facial attributes are instrumental in semantically character- izing faces. ...
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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 ...neural networks, generator and discriminator, in a minimax two-player ...
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Generative models for natural images
... Prologue to the second article Title: Improved training of Wasserstein GANs. Authors: Ishaan Gulrajani, Faruk Ahmed, Martin Arjovsky, Vincent Dumoulin, Aaron Courville. Abstract Generative adversarial ...
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A RESIDUAL DENSE GENERATIVE ADVERSARIAL NETWORK FOR PANSHARPENING WITH GEOMETRICAL CONSTRAINTS
... problem. Generative Adversarial Networks (GANs) are a class of unsupervised learning algorithms introduced by Goodfellow et ...a generative mo- del where two networks are competing each ...
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