ArcFace: Additive Angular Margin Loss for Deep Face ...

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

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.

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

HEALTHY APPROACH TO WEIGHT

A healthy approach to weight loss will include some or all of the following approaches: 1) good nutrition, 2) diet or weight loss programs, 3) exercise and movement, 4) supplements and herbs, 5) medication ... weight loss, keeping a log of the food eaten each day and weighing oneself regularly have . A Healthy Approach to Weight

  Loss, Weight, Weight loss

Circle Loss: A Unified Perspective of Pair Similarity ...

function (e.g., softmax cross-entropy loss [25, 16, 36]) to optimize the similarity between samples and weight vec-tors. The latter leverages a metric loss function (e.g., triplet loss [9, 22]) to optimize the similarity between samples. In our interpretation, there is no intrinsic difference between these two learning approaches.

  Loss, Weight

OPTAVIA® Optimal Weight 5 & 1 Plan® Journal

weight loss. The OPTAVIA for Teens plan is the only OPTAVIA Program appropriate for teens (13 to 17 years of age). The Optimal Weight 5 & 1 Plan is NOT appropriate for teens, sedentary older adults (65 years and older), nursing mothers, …

  Loss, Weight, Weight loss

Quick Guide - Tips to Reach and Stay at a Healthy Weight

changes can help with weight loss. ... Write these details in your Food Log. uick Guide 4 Tip 4: Add activity! It burns calories Staying physically active can help you reach and stay at a healthy weight. It . makes sense—staying active helps you burn some of the calories

  Guide, Tips, Quick, Loss, Reach, Healthy, Weight, Weight loss, Stay, Quick guide tips to reach and stay at a healthy weight, Stay at a healthy weight

HAMILTON ANXIETY SCALE - Assessment Psychology

• Weight loss • Abdominal fullness 12. GENITOURINARY SYMPTOMS • Urinary frequency or urgency • Dysmenorrhea • Impotence 13. AUTONOMIC SYMPTOMS • Dry Mouth • Flushing • Pallor • Sweating 14. BEHAVIOR AT INTERVIEW • Fidgets • Tremor • Paces Score HAMA_form.lay 7/14/06 3:11 PM Page 1

  Loss, Anxiety, Hamilton anxiety, Hamilton, Weight, Weight loss

Loss-on-Ignition Standard Operating Procedure

Weight loss is measured after heating the samples overnight at 100ºC to remove water, at 550ºC for four hours to remove organic matter, and at ... Log into the Analytical database, click LOI Requests/QUEUE” and create a new record for each separate project you are working on. Fill in Expedition, lake, year and number of samples. ...

  Loss, Ignition, Weight, Weight loss, Loss on ignition

HOW TO FILE A FREIGHT CLAIM FOR LOSS OR DAMAGE

Some carriers may require a “Bond of Indemnity” to be filed with the claim indemnifying the carrier for any loss it may suffer if it pays the wrong party. Claims should be numbered (BOL number, etc.) by the claimant and recorded in a claim log or computer system.

  Claim, Loss, Freight, Freight claim, Claim log

Notes on Backpropagation

sum-of-squared loss function). With this combination, the output prediction is always between zero and one, and is interpreted as a probability. Training corresponds to maximizing the conditional log-likelihood of the data, and as we will see, the gradient calculation simplifies nicely with this combination.

  Notes, Loss, Notes on backpropagation, Backpropagation