CartoonGAN: Generative Adversarial Networks for Photo ...

is 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-

  Network, Adversarial, Generative, Generative adversarial networks

Finding Tiny Faces in the Wild With Generative Adversarial ...

faces, which are unfriendly for the face classifier. Toward-s this end, we design a refinement sub-network to recover some detailed information. In the discriminator network, the basic GAN [17, 12, 8] is trained to distinguish the real and fake high resolution images. To classify faces or non-

  Faces

Squeeze-and-Excitation Networks - openaccess.thecvf.com

Squeeze-and-Excitation Networks Jie Hu1∗ Li Shen2∗ Gang Sun1 hujie@momenta.ai lishen@robots.ox.ac.uk sungang@momenta.ai 1 Momenta 2 Department of Engineering Science, University of Oxford Abstract Convolutional neural networks are built upon the con-

  Network, Excitation, Squeeze and excitation networks, Squeeze

What Have We Learned From Deep Representations for …

what these powerful models actually have learned. In this paper we shed light on deep spatiotemporal net-works by visualizing what excites the learned models us-ing activation maximization by backpropagating on the in-put. We are the first to visualize the hierarchical features

  What, Have, Learned, What we have learned, Have learned

RegularFace: Deep Face Recognition via Exclusive ...

RegularFace: Deep Face Recognition via Exclusive Regularization Kai Zhao Jingyi Xu Ming-Ming Cheng ∗ TKLNDST, CS, Nankai University kaiz.xyz@gmail.com cmm@nankai.edu.cn

  Zhao

Protecting World Leaders Against Deep Fakes

Protecting World Leaders Against Deep Fakes Shruti Agarwal and Hany Farid University of California, Berkeley Berkeley CA, USA {shrutiagarwal, hfarid}@berkeley.edu

Auto-DeepLab: Hierarchical Neural Architecture Search for ...

Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation Chenxi Liu1∗, Liang-Chieh Chen 2, Florian Schroff2, Hartwig Adam2, Wei Hua2, Alan Yuille1, Li Fei-Fei3 1Johns Hopkins University 2Google 3Stanford University Abstract Recently, NeuralArchitectureSearch(NAS)hassuccess-

PointNet: Deep Learning on Point Sets ... - CVF Open Access

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation Charles R. Qi* Hao Su* Kaichun Mo Leonidas J. Guibas Stanford University

  Open, Learning, Points, Deep, Sets, Pointnet, Deep learning on point sets

Frustum PointNets for 3D Object Detection From RGB-D Data

Frustum PointNets for 3D Object Detection from RGB-D Data Charles R. Qi1∗ Wei Liu2 Chenxia Wu2 Hao Su3 Leonidas J. Guibas1 1Stanford University 2Nuro, Inc. 3UC San Diego Abstract In this work, we study 3D object detection from RGB-D data in both indoor and outdoor scenes.

Class-Balanced Loss Based on Effective Number of Samples

and large-scale datasets including ImageNet and iNatural-ist. Our results show that when trained with the proposed class-balanced loss, the network is able to achieve signifi-cant performance gains on long-tailed datasets. 1. Introduction The recent success of deep Convolutional Neural Net-works (CNNs) for visual recognition [26, 37, 38, 16] owes

  Scale, Visual, Recognition, Visual recognition

CartoonGAN: Generative Adversarial Networks for Photo ...

is 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-

  Network, Adversarial, Generative, Generative adversarial networks

Deep Learning on Graphs - Michigan State University

9.3 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

  Network, Learning, Deep, Graph, Adversarial, Generative, Generative adversarial networks, Deep learning on graphs

Time-series Generative Adversarial Networks

A 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 ...

  Network, Adversarial, Generative, Generative adversarial networks

Wasserstein Generative Adversarial Networks

Wasserstein Generative Adversarial Networks Figure 1: These plots show ˆ(P ;P 0) as a function of when ˆis the EM distance (left plot) or the JS divergence (right plot).The EM plot is continuous and provides a usable gradient everywhere.

  Network, Adversarial, Generative, Wasserstein generative adversarial networks, Wasserstein

Self-Attention Generative Adversarial Networks

Self-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.

  Network, Self, Attention, Adversarial, Generative, Self attention generative adversarial networks

Labels to Street Scene Labels to Facade BW to Color

exactly 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

  Network, Adversarial, Generative, Generative adversarial networks

NANODEGREE PROGRAM SYLLABUS Deep Learning

Zhu, 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

  Programs, Network, Syllabus, Learning, Deep, Adversarial, Generative, Generative adversarial networks, Nanodegree program syllabus deep learning, Nanodegree

Generative Adversarial Nets - NIPS

Generative 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.

  Network, Adversarial, Generative, Generative adversarial, Generative adversarial networks, Adversar ial, Adversar

Generative Adversarial Imitation Learning

networks [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

  Network, Learning, Adversarial, Generative, Imitation, Generative adversarial, Generative adversarial imitation learning