Search results with tag "Generative adversarial networks"
CartoonGAN: Generative Adversarial Networks for Photo ...
openaccess.thecvf.comis to use Generative Adversarial Networks (GANs) [9, 34], which produce state-of-the-art results in many applications suchastexttoimagetranslation[24],imageinpainting[37], image super-resolution [19], etc. The key idea of a GAN model is to train two networks (i.e., a generator and a dis-criminator) iteratively, whereby the adversarial loss pro-
Quantum Deep Learning for Mutant COVID-19 Strain Prediction
arxiv.orgtum eigensolvers, quantum convolutional neural networks [8], quantum generative adversarial networks(GAN) [9,32] and quantum reinforcement learning [13] have been developed. Addi-tionally, quantum style-based generative adversarial networks will be proposed to better predict COVID-19 epidemic strains.
“Deep Fakes” using Generative Adversarial Networks (GAN)
noiselab.ucsd.edustyles. Generative adversarial networks (GANs) provide us an available way to implement “Deep Fakes”. In this project, we use a Cycle-GAN network which is a combination of two GAN networks . The loss can be divided into 2 parts: total generator loss L G and discriminator loss L D, where L G includes a cycle-consistency loss L cyc to en-
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 Learning on Graphs - Michigan State University
cse.msu.edu9.3 Recurrent Neural Networks on Graphs 191 9.4 Variational Autoencoders on Graphs 193 9.4.1 Variational Autoencoders for Node Represen-tation Learning 195 9.4.2 Variational Autoencoders for Graph Generation 196 9.5 Generative Adversarial Networks on Graphs 199 9.5.1 Generative Adversarial Networks for Node Representation Learning 200
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, …
Labels to Street Scene Labels to Facade BW to Color
arxiv.orgexactly what is done by the recently proposed Generative Adversarial Networks (GANs) [24,13,44,52,63]. GANs learn a loss that tries to classify if the output image is real or fake, while simultaneously training a generative model to minimize this loss. Blurry images will not be tolerated since they look obviously fake. Because GANs learn a loss
StackGAN: Text to Photo-Realistic Image Synthesis With ...
openaccess.thecvf.comGenerative Adversarial Networks (GAN) [8, 5, 23] have shown promising results in synthesizing real-world im-ages. Conditioned on given text descriptions, conditional-This bird is white with some black on its head and wings, and has a long orange beak This bird has a yellow belly and tarsus, grey back, wings, and brown throat, nape with a black face
NANODEGREE PROGRAM SYLLABUS Deep Learning
d20vrrgs8k4bvw.cloudfront.netZhu, inventors of types of generative adversarial networks, as well as AI experts, Sebastian Thrun and Andrew Trask. For anyone interested in this transformational technology, this program is an ideal point-of-entry. The program is comprised of 5 courses and 5 projects. Each project you build will be an opportunity to
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.
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Generative Adversarial Networks arXiv:1809.00219v2 [cs.CV] …
arxiv.orgESRGAN: Enhanced Super-Resolution Generative Adversarial Networks Xintao Wang 1, Ke Yu , Shixiang Wu2, Jinjin Gu3, Yihao Liu4, Chao Dong 2, Chen Change Loy5, Yu Qiao , Xiaoou Tang1 1CUHK-SenseTime Joint Lab, The Chinese University of Hong Kong 2SIAT-SenseTime Joint Lab, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences 3The …