Perceptual Generative Adversarial Networks for Small ...

ESRGAN: Enhanced Super-Resolution Generative Adversarial ...

ESRGAN: 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, …

  Network, Adversarial, Generative, Generative adversarial, 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

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

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

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

Generative Adversarial Imitation Learning - NeurIPS

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 we find that it outperforms competing methods by a wide margin in training policies for complex,

  Training, Learning, Adversarial, Generative, Imitation, Generative adversarial imitation learning, Adversarial training

Generative Adversarial Networks arXiv:1809.00219v2 [cs.CV ...

Throughout the literature, photo-realism is usually attained by adversarial training with GAN [15]. Recently there are a bunch of works that focus on de-veloping more e ective GAN frameworks. WGAN [31] proposes to minimize a reasonable and e cient approximation of Wasserstein distance and regularizes discriminator by weight clipping.

  Training, Adversarial, Adversarial training

arXiv:1411.1784v1 [cs.LG] 6 Nov 2014

the adversarial training framework allows for considerable flexibility in how this hidden representa-tion is composed. 1 In the discriminator x and y are presented as inputs and to a discriminative function (embodied again by a MLP in this case).

  Training, Adversarial, Adversarial training

Adversarial Examples and Adversarial Training

May 30, 2017 · 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. Adversarially trained

  Training, Adversarial, Adversarial training

“Deep Fakes” using Generative Adversarial Networks (GAN)

sarial, and they play two adversarial roles like a forger and a detective literally. After the training period, the generator can produce fake images with high fidelity. In 2015, Rad-ford et al. used a deep convolutional generative adversarial network …

  Training, Network, Using, Deep, Efka, Adversarial, Generative, Deep fakes using generative adversarial networks

A Taxonomy and Terminology of Adversarial Machine Learning

180 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

  Training, Adversarial

Adversarial Discriminative Domain Adaptation

Adversarial 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

  Training, Adversarial

JOURNALISM, ‘FAKE NEWS’ & DISINFORMATION - UNESCO

Handbook for Journalism Education and Training Cherilyn Ireton and Julie Posetti. TABLE OF CONTENTS Foreword by Guy Berger 7 Introduction by Cherilyn Ireton and Julie Posetti 14 Using this handbook as a model curriculum by Julie Posetti 26 MODULE 1: Truth, trust and journalism: why it matters 32 by Cherilyn Ireton

  Training, News, Efka, Fake news