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

Zero-Reference Deep Curve Estimation for Low-Light Image …

Zero-Reference Deep Curve Estimation for Low-Light Image Enhancement Chunle Guo1,2∗ Chongyi Li1,2∗ Jichang Guo1† Chen Change Loy3 Junhui Hou2 Sam Kwong2 Runmin Cong4 1 BIIT Lab, Tianjin University 2 City University of Hong Kong 3 Nanyang Technological University 4 Beijing Jiaotong University {guochunle,lichongyi,jcguo}@tju.edu.cn ccloy@ntu.edu.sg

  Estimation

Simple and Scalable Predictive Uncertainty Estimation using …

tainty estimation and compare their performance to current state-of-the-art approximate Bayesian methods on a series of classification and regression benchmark datasets. Compared to Bayesian NNs (e.g. variational inference or MCMC methods), our method is …

  Estimation, Uncertainty, The art, Uncertainty estimation

MATHEMATICS KINDERGARTEN TO GRADE 9 - Alberta

Estimation is used for determining approximate values or quantities or for determining the reasonableness of calculated values. It often uses benchmarks or referents. Students need to know when to estimate, how to estimate and what strategy to use.

  Alberta, Estimation

Age and Gender Classification using Convolutional Neural …

benchmark for age and gender estimation and show it to dramatically outperform current state-of-the-art methods. 1. Introduction Age and gender play fundamental roles in social inter-actions. Languages reserve different salutations and gram-mar rules for men or women, and very often different vo-cabularies are used when addressing elders ...

  Using, Gender, Estimation, Classification, Neural, Convolutional, The art, And gender classification using convolutional neural

La gestion des arrêts de travail : L’estimation des IJSS

Cette disposition ne s’applique pas en cas d’absence non autorisée (art R323-8 du code de la sécurité sociale). Pour les apprentis, jusqu’en 2019, les indemnités de sécurité sociale étaient calculées sur une base forfaitaire. En 2019, les bases forfaitaires sont supprimées.

  Estimation

Cost Estimating Manual for projects

More of an art than a science, cost estimating requires a thorough understanding of project scope, past price history, and current market conditions, as well as with a generous application of human judgment. Key goals of the estimating process described in this manual include: 1. Departmentwide priority on estimating, managing, and controlling ...

Depth Map Prediction from a Single Image using a Multi …

state-of-the-art results on both NYU Depth and KITTI, and matches detailed depth boundaries without the need for superpixelation. 1 Introduction Estimating depth is an important component of understanding geometric relations within a scene. In turn, such relations help provide richer representations of objects and their environment, often lead-

  The art

Dropout as a Bayesian Approximation: Representing Model …

art methods. Lastly we give a quantitative assessment of model uncertainty in the setting of reinforcement learning, on a practical task similar to that used in deep reinforce-ment learning (Mnih et al.,2015).1 2. Related Research It has long been known that infinitely wide (single hid-den layer) NNs with distributions placed over their weights

  Dropout, Uncertainty, Bayesian