Generative adversarial
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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 ...
Wasserstein Generative Adversarial Networks
proceedings.mlr.pressWasserstein Generative Adversarial Networks the other hand, training GANs is well known for being del-icate and unstable, for reasons theoretically investigated in (Arjovsky & Bottou,2017). In this paper, we direct our attention on the various ways to measure how close the model distribution and the real dis-
ESRGAN: Enhanced Super-Resolution Generative Adversarial ...
openaccess.thecvf.comAbstract. The Super-Resolution Generative Adversarial Network (SR-GAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details are often accompanied with unpleasant artifacts. To further enhance the visual quality, we thoroughly study three key components of SR-
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
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
“Deep Fakes” using Generative Adversarial Networks (GAN)
noiselab.ucsd.eduGenerative 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
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.
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 ...
A Simple Unified Framework for Detecting Out-of ...
proceedings.neurips.ccdetecting adversarial samples in a sense, but do not utilize the Mahalanobis distance-based metric, i.e., they only utilize the Euclidean distance in their scores. In this paper, we show that Mahalanobis distance is significantly more effective than the Euclidean distance in various tasks. Experimental supports for generative classifiers.
Deep Learning on Graphs - Michigan State University
cse.msu.edu6.3 Graph Adversarial Defenses 151 6.3.1 Graph Adversarial Training 152 6.3.2 Graph Purification 154 6.3.3 Graph Attention 155 6.3.4 Graph Structure Learning 159 6.4 Conclusion 160 6.5 Further Reading 160 7 Scalable Graph Neural Networks 162 7.1 Introduction 162 7.2 Node-wise Sampling Methods 166 7.3 Layer-wise Sampling Methods 168