ShuffleNet V2: Practical Guidelines for Efficient CNN ...

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

LightGBM: A Highly Efficient Gradient Boosting Decision …

LightGBM: A Highly Efficient Gradient Boosting Decision Tree Guolin Ke 1, Qi Meng2, Thomas Finley3, Taifeng Wang , Wei Chen 1, Weidong Ma , Qiwei Ye , Tie-Yan Liu1 1Microsoft Research 2Peking University 3 Microsoft Redmond 1{guolin.ke, taifengw, wche, weima, qiwye, tie-yan.liu}@microsoft.com; 2qimeng13@pku.edu.cn; 3tfinely@microsoft.com; Abstract …

  Microsoft, Decision, Tree, Boosting, Highly, Derating, Efficient, Highly efficient gradient boosting decision, Highly efficient gradient boosting decision tree

High-Performance Building Envelopes: Design Methods for ...

FOR ENERGY EFFICIENT FACADES Ajla Aksamija, PhD, LEED AP BD+C, CDT 1. ABSTRACT . The objective of this paper is to discuss design methods for sustainable, high- performance facades, and the necessary steps in ensuring that the environmental factors and energy - efficiency strategies are integrated with the design process. The facade is one of ...

  Efficient

fzhangxiangyu,zxy,linmengxiao,sunjiang@megvii.com arXiv ...

a highly efficient basic architecture specially designed for our desired computing ranges. We notice that state-of-the-art basic architectures such as Xception [3] and ResNeXt [40] become less efficient in ex-tremely small networks because of the costly dense 1 1 convolutions. We propose using pointwise group convolu-* Equally contribution.

  Highly

Complete Parking Lot Design - PDHonline.com

plan, balancing the cut and fill is highly encouraged when it does not result in further damage to the natural topography. Where the site plan results in substantial off- haul or infill, a mitigation plan may be required to address the impacts and clean-up of off-site construction activities. To

  Parking, Highly

Massachusetts Model System for Educator Evaluation

Massachusetts Model System for Educator Evaluation CLASSROOM TEACHER RUBRIC August 2018 75 Pleasant Street, Malden, MA02148-4906 Phone 781 …

TM-T88V Technical Reference Guide

4 Warnings WARNING • To avoid risk of electric shock, do not set up this product or handle cables during a thunderstorm • Never insert or disconnect the power plug with wet hands. Doing so may result in severe shock. • Handle the power cable with care. Improper handling may lead to fire or electric shock. ∗Do not modify or attempt to repair the cable.

Federal Data Strategy 2020 Action Plan

Federal Data Strategy 2323 Action Plan 5 • Grace Levin, Management Analyst, U .S . State Department • Anne Levine, Acting Chief Data Officer, Federal Communications Commission • Lisa Mancino, Senior Agricultural Economist, Food Economic Division, Economic Research Service,

  Data, Strategy, Data strategy