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

Optimization Methods in Finance - ku

eral classes of optimization problems (including linear, quadratic, integer, dynamic, stochastic, conic, and robust programming) encountered in nan-cial models. For each problem class, after introducing the relevant theory (optimality conditions, duality, etc.) and e …

  Finance, Methods, Dynamics, Problem, Optimization, Optimization problems, Optimization methods in finance

Survey of Dispatching Rules for Schedule Optimization

1 Survey of Dispatching Rules for Schedule Optimization By Steve Morrison, Ph.D. 1997 Info@MethodicalMiracles.com 214-769-9081 Dispatching rules are a very common means of scheduling due to their simplicity, speed, and

  Rules, Schedule, Optimization, Dispatching, Dispatching rules for schedule optimization

Dynamic optimization - michaelcarteronline.com

CHAPTER 7. DYNAMIC OPTIMIZATION 3 The left-hand side is the marginal benefit of consumption today. For an optimal alloca-tion, this must be equal to the marginal cost of …

  Dynamics, Optimization, Dynamic optimization

ANALYTICAL OPTIMIZATION OF SIMPLE ROOF SHADING …

ANALYTICAL OPTIMIZATION OF SIMPLE ROOF SHADING DEVICES Khaled Nassar1and Mohamed Aly1 1The American University in Cairo line below should be left ABSTRACT Parametric ...

  Roof, Simple, Shading, Analytical, Optimization, Analytical optimization of simple roof shading

Clock Gating for Power Optimization in ASIC Design Cycle ...

JS/BGM – ISLPED08 AGENDA • Introduction • Combinational Clock Gating – State of the art – Open problems • Sequential Clock Gating – State of the art

A Lecture on Model Predictive Control

Local Optimization • A separate steady-state optimization to determine steady-state targets for the inputs and outputs; RMPCT introduced a dynamic optimizer recently • Linear Program (LP) for SS optimization; the LP is used to enforce input and output constraints and determine optimal input and output targets for the thin and fat plant cases

  Model, Control, Dynamics, Predictive, Optimization, Model predictive control

Introduction to Mathematical Optimization

Types of Optimization Problems • Some problems have constraints and some do not. • There can be one variable or many. • Variables can be discrete (for example, only have integer values) or continuous. •Some problems are static (do not change over time) while some are dynamic (continual adjustments must be made as changes occur).

  Dynamics, Problem, Optimization, Optimization problems

Cisco SD-WAN Design Guide

Dynamic optimization of SaaS applications, resulting in improved application performance for users. Rich analytics with visibility into applications and infrastructure, which enables rapid troubleshooting and assists in forecasting and analysis for effective resource planning

  Cisco, Dynamics, Optimization, Dynamic optimization

Dynamic Programming 11 - MIT - Massachusetts Institute of ...

Dynamic Programming 11 Dynamic programming is an optimization approach that transforms a complex problem into a sequence of simpler problems; its essential characteristic is the multistage nature of the optimization procedure. More so than the optimization techniques described previously, dynamic programming provides a general framework

  Dynamics, Problem, Optimization

Tools for Analysis of Dynamic Systems: Lyapunov s Methods

13 Objectives of Analysis of Nonlinear Systems Similar to the objectives pursued when investigating complex linear systems Not interested in detailed solutions, rather one seeks to characterize the system behavior---equilibrium points and their

  Analysis, System, Methods, Dynamics, Lyapunov, For analysis of dynamic systems, Lyapunov s methods