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

Fluid Mechanics II Viscosity and shear stresses

10.1 Incompressible, steady and uniform laminar flow between par-allel plates 10.2 Incompressible, steady and uniform laminar flow in circular cross-section pipes 7. Problems: 1. A uniform film of oil 0.13mm thick separates two circular discs, each 150mm diameter and mounted coaxially. Find the torque required to rotate one disc

  Lella, Par allel

Producer Theory - Stanford University

many competitors who produce perfectly substitutable products, and a par-allel condition applies to input markets. Of course, even the most casual empiricism suggests that many firms sell differentiated products and have at 1. least some flexibility in setting prices, and even small firms may have market

  Lella, Par allel

Finding Unknown Angles

teachers and textbooks introduce facts about angles within triangles and polygons, about par-allel lines, about congruent and similar figures, and about circles. These are not simply facts to memorize: understanding emerges as students use them to solve problems. Thus teaching centers on solving problems.

  Lella, Par allel

Examples of Solved Problems for Chapter3,5,6,7,and8

Also recall that when transistors are connected in par-allel their widths are effectively added, leading to an increase in drive strength. Similarly, when transistors are connected in series, their lengths are added, leading to a decrease in drive strength. Let us assume that all NMOS and PMOS transistors have the same length, Ln = Lp = L.For

  Lella, Par allel

APPENDIX C FOUNDATION CAPACITIES TABLES

capacity is controlled by bearing of wood par-allel to grain against the anchor bolt, and then the spacing of those bolts. A 1600 psi end grain bearing allowable stress was assumed, since it would cover most typical species. Thus, the capacity per bolt = 1/2” dia. × 1.5” × 1600 psi = 1200 lb. The APA Plywood Diaphragm

  Lella, Par allel