Search results with tag "Adversarial"
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.
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
Generative Adversarial Nets
arxiv.orga generative machine by back-propagating into it include recent work on auto-encoding variational Bayes [20] and stochastic backpropagation [24]. 3 Adversarial nets The adversarial modeling framework is most straightforward to apply when the models are both …
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, …
Domain Generalization With Adversarial Feature Learning
openaccess.thecvf.comDomain Generalization with Adversarial Feature Learning Haoliang Li1 Sinno Jialin Pan2 Shiqi Wang3 Alex C. Kot1 1School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 2School of Computer Science and Engineering, Nanyang Technological University, Singapore 3Department of Computer Science, City University of …
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
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 ...
Generative Adversarial Imitation Learning - NeurIPS
proceedings.neurips.ccmodeling 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 we find that it outperforms competing methods by a wide margin in training policies for complex,
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-
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
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.
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 …
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
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.
Generative Adversarial Nets - NeurIPS
proceedings.neurips.ccgenerator network with a second neural network. Unlike generative adversarial networks, the sec-ond network in a VAE is a recognition model that performs approximate inference. GANs require differentiation through the visible units, and thus cannot model discrete data, while VAEs require
Self-Attention Generative Adversarial Networks
proceedings.mlr.pressSelf-Attention Generative Adversarial Networks Figure 1. The proposed SAGAN generates images by leveraging complementary features in distant portions of the image rather than local regions of fixed shape to generate consistent objects/scenarios. In each row, the first image shows five representative query locations with color coded dots.
Perceptual Generative Adversarial Networks for Small ...
openaccess.thecvf.comPerceptual Generative Adversarial Networks for Small Object Detection Jianan Li1 Xiaodan Liang2 Yunchao Wei3 Tingfa Xu1∗ Jiashi Feng 3 Shuicheng Yan3,4 1 Beijing Institute of Technology 2 CMU 3 National University of Singapore 4 360 AI Institute {20090964, ciom xtf1}@bit.edu.cn xiaodan1@cs.cmu.edu {eleweiyv, elefjia}@nus.edu.sg yanshuicheng@360.cn
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.
A U-Net Based Discriminator for Generative Adversarial ...
openaccess.thecvf.comA U-Net Based Discriminator for Generative Adversarial Networks Edgar Schonfeld¨ Bosch Center for Artificial Intelligence edgar.schoenfeld@bosch.com
Time-series Generative Adversarial Networks - NeurIPS
proceedings.neurips.ccforecasting, this approach is fundamentally deterministic, and is not truly generative in the sense that ... TimeGAN is a generative time-series model, trained adversarially and jointly via a learned embedding space with both supervised and unsupervised losses. As such, our approach straddles the intersection ...
The Adversarial System vs. The Inquisitorial System
www.cbl-international.comThe Adversarial System vs. The Inquisitorial System Yan Yu, Nankai University, School of Law
A Taxonomy and Terminology of Adversarial Machine Learning
nvlpubs.nist.gov180 training data, and adversarial exploitation of model sensitivities to adversely affect the 181 performance of ML classification and regression. AML is concerned with the design of ML 182 algorithms that can resist security challenges, the study of the capabilities of attackers, and the
Certified Adversarial Robustness via Randomized Smoothing
proceedings.mlr.pressCertified Adversarial Robustness via Randomized Smoothing Jeremy Cohen 1Elan Rosenfeld J. Zico Kolter1 2 Abstract We show how to turn any classifier that classifies well under Gaussian noise into a new classifier
A arXiv:1412.6572v3 [stat.ML] 20 Mar 2015
arxiv.orgPublished as a conference paper at ICLR 2015 EXPLAINING AND HARNESSING ADVERSARIAL EXAMPLES Ian J. Goodfellow, Jonathon Shlens & Christian Szegedy Google Inc., Mountain View, CA fgoodfellow,shlens,szegedyg@google.com
AttnGAN: Fine-Grained Text to Image Generation …
arxiv.orgAttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks Tao Xu 1, Pengchuan Zhang2, Qiuyuan Huang2, Han Zhang3, Zhe Gan4, Xiaolei Huang1, Xiaodong He2
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
MECANISMOS ALTERNATIVOS DE SOLUCION DE …
www.gdca.com.mx5. Postura de A posturas de las partes están tan alejadas que proscriben una solución intermedia, por lo cual lo más conveniente es proceder a un medio adversarial para resolver la controversia,
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
SPRING 2013 - Building a better working world
www.ey.com2 3 A TABLE OF CONTENTS Introduction 3 Customer Service to Customer Relationship Management 5 Adversarial to Collaborative Relationships 10 Incremental Change to a ...
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
InfoGAN: Interpretable Representation Learning by ... - NIPS
papers.nips.ccInformation Maximizing Generative Adversarial Nets Xi Chen yz, Yan Duan , Rein Houthooft , John Schulman , Ilya Sutskeverz, Pieter Abbeelyz yUC Berkeley, Department of Electrical Engineering and Computer Sciences zOpenAI Abstract This paper describes InfoGAN, an information-theoretic extension to the Gener-
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
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 ...
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
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 Fakes” using Generative Adversarial Networks (GAN)
noiselab.ucsd.eduFigure 5. A demonstration of cycle-GAN [4] Figure 6. Demonstration of results for handbag-backpack trans-lation using Cycle-GAN. Left column for real images and right column for generated images loss for the whole cycle-GAN as L GAN, and we combine the discriminator losses together to get the discriminator loss for the whole network as L D ...
Introduction to Generative Adversarial Networks
www.iangoodfellow.comthen show in section 4.2 that Algorithm 1 optimizes Eq 1, thus obtaining the desired result. 3 Data Model distribution Optimal D(x) for any pdata(x) and pmodel(x) is always z x Discriminator Estimating this ratio using supervised learning is the key approximation mechanism used by …
VIBE: Video Inference for Human Body Pose and Shape …
openaccess.thecvf.compaired information by training a sequence-based generative adversarial network (GAN) [18]. Here, given the video of a person, we train a temporal model to predict the parame-ters of the SMPL body model for each frame while a mo-tion discriminator tries to distinguish between real and re-gressedsequences. Bydoingso,theregressorisencouraged
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 ...
Adversarial Examples and Adversarial Training
cs231n.stanford.eduMay 30, 2017 · (Goodfellow 2016) Adversarial Training of other Models • Linear models: SVM / linear regression cannot learn a step function, so adversarial training is less useful, very similar to weight decay • k-NN: adversarial training is prone to overfitting. • Takeway: neural nets can actually become more secure than other models.
Adversarial Discriminative Domain Adaptation
openaccess.thecvf.comAdversarial learning methods are a promising approach to training robust deep networks, and can generate complex samples across diverse domains. They can also improve recognition despite the presence of domain shift or dataset bias: recent adversarial approaches to unsupervised domain adaptation reduce the difference between the training and
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
Adversarial Generative Nets: Neural Network …
arxiv.orgAdversarial Generative Nets: Neural Network Attacks on State-of-the-Art Face Recognition Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer Carnegie Mellon University
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