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

Today: How do caches work? - University of Washington

Loading a block into the cache After data is read from main memory, putting a copy of that data into the cache is straightforward. —The lowest k bits of the address specify a cache block. —The upper (m-k) address bits are stored in the block’s tag field. —The data from main memory is stored in the block’s data field.

  Block, Cache, Cache block

Machine Learning Basic Concepts - edX

Data everywhere! 1. Google: processes 24 peta bytes of data per day. 2. Facebook: 10 million photos uploaded every hour. 3. Youtube: 1 hour of video uploaded every second.

The Academic Word List - edX

The Academic Word List The Academic Word List (AWL) was released in the year 2000 by Averil Coxhead from the School of Linguistics and Applied Language Studies at Victoria University of Wellington, New

  Academic, Lists, Words, The academic word list, The academic word list the academic word list

ETHEREUM: A SECURE DECENTRALISED GENERALISED …

a simple application on a decentralised, but singleton, compute resource. We can call this paradigm a transactional singleton machine with shared-state. ... block, which includes a series of transactions amongst some other components; and is the block-level state-transition

  Compute, Block, Ethereum

Shear Stress Example - University of Washington

thickness, and thereby compute the shear stress. Hide Text 36 Applying equilibrium to the little block gives the equation above. Note that shear flow is related to shear along the beam. Shear Stress Example: 9 (3/30/00)

  University, Example, Shares, Washington, Stress, Compute, Block, University of washington, Shear stress example

Pipeline ADC Block Diagram - University of California ...

Block Diagram •Idea: Cascade several low resolution stages to obtain high overall resolution (e.g. 10bit ADC can be built with series of 10 ADCs each 1-bit only!)

  Block

Unit Rates (with Fractions) - Marlboro Central High School

Learning Target: I can compute unit rates associated with ratios of fractions It took Jenny 2 hours to knit of a scarf. How many hours will it take for her to make one scarf? 1. Set up the ratio 2. Simplify 3. Label with appropriate units 1 3 Example #1 - Practice Mike read of the newspaper in of an hour. How long did

  Compute

031-2008: PROC REPORT: Compute Block Basics -- Part I ...

The compute block can be placed anywhere within the REPORT step, however generally compute blocks are grouped following the DEFINE statements. USING THE LINE STATEMENT One of the more interesting programming statements within the compute block is the LINE statement. This statement is

  Report, Corps, Compute, Block, Proc report, Compute block, The compute block