Dynamic Attentive Graph Learning for Image Restoration

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

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

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

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

Basic Pediatric Mechanical Ventilation Settings for ...

Basic Pediatric Mechanical Ventilation Settings for getting started: Volume Ventilation Mode SIMV/VC 1. FiO2 - 50%, if sick 100%. Wean rapidly to FiO2 < 50% if possible. 2. Inspiratory time (I time)- minimum 0.5 seconds, ranging up to 1 second in older kids

29 CONNECTION DESIGN – DESIGN REQUIREMENTS

Residual stresses and strains 2.1 Complexity of connection geometry The geometry of connections is usually more complex than that of the members being joined (Fig.1). The stress analysis of the joint is complicated by the (locally) highly indeterminate nature of the joint, non-linear nature of the behaviour due to lack of fit,

  Design, Requirements, Connection, Residual, Connection design design requirements

Alex Alemi arXiv:1602.07261v2 [cs.CV] 23 Aug 2016

the Impact of Residual Connections on Learning Christian Szegedy Google Inc. 1600 Amphitheatre Pkwy, Mountain View, CA szegedy@google.com Sergey Ioffe sioffe@google.com Vincent Vanhoucke vanhoucke@google.com Alex Alemi alemi@google.com Abstract Very deep convolutional networks have been central to the largest advances in image recognition ...

  Learning, Residual

Policy Gradient Methods for Reinforcement Learning with ...

residual-gradient, temporal-difierence, and dynamic-programming methods. ... Learning a value function and using it to reduce the variance of the gradient estimate appears to be essential for rapid learning. Jaakkola, Singh and Jordan (1995) proved a result very similar to ours for the special case of function ...

  Methods, Learning, Residual, Reinforcement, Derating, Gradient methods for reinforcement learning

Zero-Inflated Negative Binomial Regression

Raw Residual The raw residual is the difference between the actual response and its expected value estimated by the model. Because we expect the variances of the residuals to be unequal, there are difficulties in the interpretation of the raw residuals. However, they are still popular. The formula for the raw residual is

  Residual

Aggregated Residual Transformations for Deep Neural …

Aggregated Residual Transformations for Deep Neural Networks Saining Xie1 Ross Girshick2 Piotr Dollar´ 2 Zhuowen Tu1 Kaiming He2 1UC San Diego 2Facebook AI Research {s9xie,ztu}@ucsd.edu {rbg,pdollar,kaiminghe}@fb.com Abstract We present a simple, highly modularized network archi-

  Residual

Welding Handbook - American Welding Society

ii Welding Handbook, Ninth Edition Volume 1 Welding Science and Technology Volume 2 Welding Processes—Part 1 Volume 3 Welding Processes—Part 2 Volume 4

  Handbook, Welding, Welding handbook

Deep Residual Learning for Image Recognition

the residual learning principle is generic, and we expect that it is applicable in other vision and non-vision problems. 2. Related Work Residual Representations. In image recognition, VLAD [18] is a representation that encodes by the residual vectors with respect to a dictionary, and Fisher Vector [30] can be

  Learning, Residual, Residual learning