Diffusion Probabilistic Models for 3D Point Cloud Generation

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

Thermodynamic Models & Physical Properties

Electrolyte NRTL, and Scatchard Hildebrand models. In these models, the activity coefficient approach is used to calculate the liquid properties, while the vapor phase properties are calculated using an equation of state. 4. Other Models: There are still many other property models available through Aspen Plus.

  Model, Property, Thermodynamics, Thermodynamic models, Property models

Chemkin Theory Manual - Pennsylvania State University

17.0 Chemkin Theory Manual Chemkin® Software CK-THE-15151-1601-UG-1 January 2016

  Manual, Theory, Chemkin theory manual, Chemkin

Lecture 1 – Introduction to Ecology

Sustainability – a property of human society in which ecosystems (including humans) ... Numerical models of the growth of individual populations ... sun?” and “Under the thermodynamic conditions of the biosphere, water is a powerful chemical agent...” but on a dead Earth, water is “...a compound of weak chemical activity?” ...

  Model, Property, Thermodynamics

CoolPack tutorial - IPU

CoolPack tutorial Page 6 The group EESCoolTools contains a large collection of programs for both refrigeration systems and components. We have chosen to divide this group into four subgroups as shown on Figure 2.2. The groups also represent the four phases of designing a refrigeration system.

Theoretical Statistical Physics

Moreover statistical physics is much more than thermodynamic equilibrium and if time permits, a course on statistical physics should also cover some aspects of non- equilibrium physics, for example the exciting recent developments in stochastic ther-

  Thermodynamics

NIST/ASME Steam Properties—STEAM

The IAPWS water/steam property models are implemented in a suite of FORTRAN subroutines. They are written in a structured format, are internally documented with extensive comments, and have been tested on a variety of compilers. Routines are provided to calculate thermodynamic and transport properties at a given (T,ρ) state.

  Model, Property, Inst, Thermodynamics, Property models

On the black hole/string transition

On the black hole/string transition Yiming Chen1, Juan Maldacena2 and Edward Witten2 1 Jadwin Hall, Princeton University, Princeton, New Jersey, USA 2 Institute for Advanced Study, Princeton, New Jersey, USA Abstract We discuss aspects of the possible transition between small black holes and highly ex-

A Matlab Toolbox For Thermodynamic Property Evaluation

2 Ideal Gases Incompressible Substances Compressible Substances Each substance has its own mathematical models used to evaluated the properties. For ideal gases the first of these models is the ideal gas law Pv RT (1) For the remaining properties we may use u - u o = cv dT = cP - R dT (2) h -h = c dT o P (3) dP P R dT - T c s -s = P o

  Model, Property, Thermodynamics, Matlab, Thermodynamic property