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

Understanding Transformer Noise - Federal Pacific

TRANSFORMER NOISE Noise is defined as unwanted sound. But, what is unwanted sound? A mel-low sound to some, can be completely unacceptable to others. Attending rock ... thus setting the noise and vibration level. Also, increasing (or decreasing) magnetization does not increase or decrease the magnetostriction by the

  Testing, Transformers

arXiv:1901.02860v3 [cs.LG] 2 Jun 2019

fixed-length context in the setting of language modeling. We propose a novel neural ar-chitecture Transformer-XL that enables learn-ing dependency beyond a fixed length with-out disrupting temporal coherence. It con-sists of a segment-level recurrence mechanism and a novel positional encoding scheme. Our method not only enables capturing ...

  Testing, Transformers

Transformer Protection Application Guide

Transformer Protection Application Guide This guide focuses primarily on application of protective relays for the protection of power transformers, with an emphasis on the most prevalent protection schemes and transformers. Principles are emphasized. Setting procedures are only discussed in a general nature in the material to follow. Refer to ...

  Protection, Testing, Transformers, Transformer protection

Point Transformer

Point Transformer Figure 1. The Point Transformer can serve as the backbone for var-ious 3D point cloud understanding tasks such as object classifica-tion, object part segmentation, and semantic scene segmentation. in natural language processing [39,45,5,4,51] and image analysis [10,28,54]. The transformer family of models is

  Transformers

Fly-back transformer design instructions Design tool

Fly-back transformer Design July,2016 DT_Fly-Back_transformer AN-174E Rev1.0 5/8 Step-4 : Operation condition under critical or continuous mode Set under what condition the transformer is to be operated on the critical or continuous mode. If the critical mode is selected, input the input volt age and output current where you want it to operate

  Transformers

Transformer asics Information Guide - Motion Labs

A transformer that is rated at 50Hz will also operate at 60Hz, however the inverse is not true: A 60Hz rated transformer cannot operate at 50Hz, unless de-rated at least 20% to allow for excessive heating effects. This is NOT recommended and these applications require a transformer rated for 50hz/60hz.

  Guide, Information, Transformers, Cisa, Transformer asics information guide

Transformer-XL: Attentive Language Models beyond a Fixed ...

⌧+1= Transformer-Layer(q n,kn ⌧+1,v n). where the function SG(·) stands for stop-gradient, the notation [hu hv] indicates the concatenation of two hidden sequences along the length dimen-sion, and W· denotes model parameters. Com-pared to the standard Transformer, the critical dif-ference lies in that the key kn ⌧+1 and value v n ⌧+1

  Transformers