Search results with tag "Generative adversarial"
生成对抗网络及其在图像生成中的 ... - ict.ac.cn
cjc.ict.ac.cnand computational challenges. At the same time, generative adversarial networks are the latest and most successful technology among generative models. Especially in terms of image generation, compared with other generation models, generative adversarial networks can not only avoid complicated calculations, but also generate better quality images.
Lecture 13: Generative Models
cs231n.stanford.eduGenerative models Explicit density Implicit density Direct Tractable density Approximate density Markov Chain Variational Markov Chain Variational Autoencoder Boltzmann Machine GSN GAN Figure copyright and adapted from Ian Goodfellow, Tutorial on Generative Adversarial Networks, 2017. Today: discuss 3 most popular types of generative models today
Time-series Generative Adversarial Networks
papers.nips.ccA good generative model for time-series data should preserve temporal dynamics, in the sense that new sequences respect the original relationships between variables across time. Existing methods that bring generative adversarial networks (GANs) into the sequential setting do not adequately attend to the temporal correlations unique to time ...
“Deep Fakes” using Generative Adversarial Networks (GAN)
noiselab.ucsd.edubased on deep convolutional GANs. 2.1. Generative Adversarial Networks (GAN) The basic module for generating fake images is a GAN. A block diagram of a typical GAN network is shown in Fig-ure2. A GAN network is consisted of a generator and a discriminator. During the training period, we use a data set Xwhich includes a large number of real ...
CartoonGAN: Generative Adversarial Networks for Photo ...
openaccess.thecvf.comtoonGAN, a generative adversarial network (GAN) frame-work for cartoon stylization. Our method takes unpaired photos and cartoon images for training, which is easy to use. Two novel losses suitable for cartoonization are pro-posed: (1) a semantic content loss, which is formulated as a sparse regularization in the high-level feature maps of
ESRGAN: Enhanced Super-Resolution Generative Adversarial …
openaccess.thecvf.comESRGAN: EnhancedSuper-Resolution Generative Adversarial Networks Xintao Wang 1, Ke Yu , Shixiang Wu2, Jinjin Gu3, Yihao Liu4, Chao Dong 2, Yu Qiao , and Chen Change Loy5 1 CUHK-SenseTime Joint Lab, The Chinese University of Hong Kong 2 Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences 3 The Chinese University of Hong Kong, …
Christian Ledig, Lucas Theis, Ferenc Huszar, Jose ...
arxiv.orga generative adversarial network (GAN) for image super-resolution (SR). To our knowledge, it is the first framework capable of inferring photo-realistic natural images for 4 upscaling factors. To achieve this, we propose a perceptual loss function which consists of an adversarial loss and a content loss. The adversarial loss pushes our solution to
CHANGE DETECTION IN REMOTE SENSING IMAGES USING ...
www.int-arch-photogramm-remote-sens-spatial-inf-sci.netKEY WORDS: Change Detection, Database, Deep Convolutional Neural Networks, Generative Adversarial Networks ABSTRACT: We present a method for change detection in images using Conditional Adversarial Network approach. The original network architecture based on pix2pix is proposed and evaluated for difference map creation.
![arXiv:1411.1784v1 [cs.LG] 6 Nov 2014](/cache/preview/f/a/6/8/3/a/5/7/thumb-fa683a5739b507f70aa173f9c218eee1.jpg)
arXiv:1411.1784v1 [cs.LG] 6 Nov 2014
arxiv.orggenerative models. In this work we introduce the conditional version of generative adversarial nets, which can be constructed by simply feeding the data, y, we wish to condition on to both the generator and discriminator. We show that this model can generate MNIST digits conditioned on class labels. We also illustrate how
GANs Trained by a Two Time-Scale Update Rule ... - NeurIPS
proceedings.neurips.ccGenerative Adversarial Networks (GANs) excel at creating realistic images with ... discriminator and the generator. Using the theory of stochastic approximation, we prove that the TTUR converges under mild assumptions to a stationary local Nash equilibrium. The convergence carries over to the popular Adam optimization, for ... gorithms based on ...
Conditional Image Synthesis with Auxiliary Classifier GANs
arxiv.orgGenerative adversarial networks (GANs) offer a distinct and promising approach that focuses on a game-theoretic formulation for training an image synthesis model (Good-fellow et al.,2014). Recent work has shown that GANs can produce convincing image samples on datasets with low variability and low resolution (Denton et al.,2015;Radford et al ...
ffirstname.lastnameg@tue.mpg.de arXiv:2011.12100v2 [cs.CV ...
arxiv.orgGAN-based Image Synthesis: Generative Adversarial Networks (GANs) [24] have been shown to allow for pho-torealistic image synthesis at resolutions of 10242 pixels and beyond [6,14,15,39,40]. To gain better control over the synthesis process, many works investigate how factors of variation can be disentangled without explicit supervi-sion.
Generative Adversarial Nets - NIPS
papers.nips.ccGenerative adversarial networks has been sometimes confused with the related concept of “adversar-ial examples” [28]. Adversarial examples are examples found by using gradient-based optimization directly on the input to a classification network, in order to find examples that are similar to the data yet misclassified.
Adversarial Sparse Transformer for Time Series Forecasting
proceedings.neurips.ccAdversarial Sparse Transformer (AST), based on Generative Adversarial Networks (GANs). Specifically, AST adopts a Sparse Transformer as the generator to learn a sparse attention map for time series forecasting, and uses a discriminator to improve the prediction performance at a sequence level. Extensive experiments on
Generative Adversarial Imitation Learning
proceedings.neurips.ccnetworks [8], a technique from the deep learning community that has led to recent successes in modeling distributions of natural images: our algorithm harnesses generative adversarial training to fit distributions of states and actions defining expert behavior. We test our algorithm in Section 6, where
Generative Adversarial Nets - arXiv
arxiv.orgAlgorithm 1 Minibatch stochastic gradient descent training of generative adversarial nets. The number of steps to apply to the discriminator, k, is a hyperparameter. We used k= 1, …
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