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

Mixtures of Normals - Princeton University Press Home Page

February 20, 2014 Time: 10:27am chapter1.tex 1 Mixtures of Normals In this chapter, I will review the mixture of normals model and discuss various methods for inference with special attention

  Mixtures of normals

DENSITY ESTIMATION FOR STATISTICS AND DATA ANALYSIS

The kernel estimator The nearest neighbour method The variable kernel method Orthogonal series estimators Maximum penalized likelihood estimators General weight function estimators Bounded domains and directional data Discussion and bibliography 1. INTROUCTION 1.1. What is density estimation? The probability density function is a fundamental ...

  Density, Kernel, Estimation, Density estimation

PROBABILITY AND STATISTICS FOR ECONOMISTS

Preface This textbook is the first in a two-part series covering the core material typically taught in a one-year Ph.D. course in econometrics.

Lecture 12 Nonparametric Regression

That is, a kernel regression estimator is a local constant regression, since it sets m(x) equal to a constant, θ, in the very small neighborhood of x0: Note: The residuals are weighted quadratically => weighted LS! ... using the density estimation results.

  Density, Kernel, Estimation, Density estimation

Kernel density estimation in R - University of New Mexico

Kernel density estimation R: violin plot The violin plot uses the function sm.density() rather than density() for the nonparametric density estimate, and this leads to smoother density estimates. If you want to modify the behavior of the violin plot, you can copy the original code to your own function and change how the

  Density, Kernel, Estimation, Kernel density estimation

Mixture Models - Carnegie Mellon University

20.1.2 From Kernel Density Estimates to Mixture Models We have also previously looked at kernel density estimation, where we approximate the true distribution by sticking a small (1 n weight) copy of a kernel pdf at each ob-served data point and adding them up. With enough data, this comes arbitrarily

  Density, Kernel, Estimation, Kernel density estimation, Kernel density

Syntax - Stata

Cox, N. J. 2005.Speaking Stata: Density probability plots. Stata Journal 5: 259–273.. 2007.Software Updates: Speaking Stata: Density probability plots. Stata Journal 7: 593. Also see [R] kdensity — Univariate kernel density estimation [G …

  Density, Kernel, Estimation, Syntax, Kernel density estimation