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

4.2 Shapes of Distributions - University of Iowa

1 4.2 Shapes of Distributions ! Symmetry " Symmetrical or asymmetrical " If symmetrical, mounded or flat? Skew " Right, left Peaks or Modes " Unimodal, bimodal, multiple peaks Spread " Narrow spread or wide spread

  Phases, Distribution, 2 shapes of distributions

Chapter 10 Joint densities - Yale University

and Y have continuous distributions, it becomes more important to have a systematic way to describe how one might calculate probabilities of the form Pf.X;Y/2Bgfor various sub- ... blobs, small shapes that don’t have any particular name—whatever suits the needs of a par-ticular calculation. <10.2> Example.

  Phases, Chapter, Distribution, Joint, Densities, Chapter 10 joint densities

Fitting distributions with R

Fitting distributions with R 8 3 ( ) 4 1 4 2--= = s m g n x n i i isP ea r o n'ku tcf . According to the value of K, obtained by available data, we have a particular kind of function. Here are some examples of continuous and discrete distributions6, they will be used afterwards in this paper.

  Distribution

Describing Distributions - With Graphs or Tables

4.2 How to Make A Histogram How many hours per week (h) will you study for STAT 220? quantitative Hours Count Proportion 0 h < 2 3 0.03 2 h < 4 15 0.17 4 h < 6 26 0.29 6 h < 8 14 0.15 8 h < 10 9 0.10 10 h < 12 15 0.17 12 h < 14 3 0.03 14 h …

  With, Distribution, Table, Graph, Describing, Describing distributions with graphs or tables

AP Statistics 2008 Scoring Guidelines - College Board

The shapes of the two distributions differ. The distribution of sugar content for three-quarter-cup cereals is reasonably symmetric: notice that the median is in the middle of the box. The distribution of sugar content for one-cup cereals is clearly skewed to …

  Phases, Distribution, Statistics, Ap statistics

AP Statistics 2011 Scoring Guidelines Form B - College Board

12–13 1 2 13–14 14 5+= 24 6+= 14–15 14 6 11++= 24 4 10++= 15–16 14 6 3 14+++= 24 4 11 21+++ = Thus, the median P-T ratio for both groups is at least 15 students per teacher and at most 16 students per teacher. Part (b): The shapes of the two histograms are different. The histogram for states that are west of the

  Phases, Statistics, Ap statistics

Chapter 4, Estimating Density: Quadrat Counts

highest statistical precision for a given total area sampled, or for a given total amount of time or money, (2) ecologically, as that quadrat size and shape that are most efficient to answer the question being asked, and (3) logistically, as that quadrat size and shape that are easiest to put out and use. You should be wary of the logistical criterion since in

  Chapter, Count, Chapter 4, Density, Estimating, Quadrats, Estimating density, Quadrat counts