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

Red Hat Enterprise Linux Network Performance Tuning Guide

Mar 25, 2015 · moving the data from kernel space to userspace and ending the kernel's involvement in the receive process. Packet egress in the Linux kernel Another important aspect of the Linux kernel is network packet egress. Although simpler than the ingress logic, the egress is still worth acknowledging. The process works when skbs are passed

  Network, Kernel

Abstract arXiv:1411.4038v2 [cs.CV] 8 Mar 2015

with kernel size and stride obeying the transformation rule f ks g k0s0 = (f g) 0+( 1)s;ss: While a general deep net computes a general nonlinear function, a net with only layers of this form computes a nonlinear filter, which we call a deep filter or fully convolu-tional network. An FCN naturally operates on an input of

  Network, Kernel

Ubuntu Linux Setup Guide - Lenovo

The wireless network driver isn’t native to the Ubuntu Linux 18.04 kernel. Therefore, the wireless device will not work following the Ubuntu 18.04 LTS installation by default. However, here are a couple of workarounds to get the wireless network working on the Thinkpad P53 and P73.

  Guide, Linux, Network, Ubuntu, Setup, Kernel, Lenovo, Ubuntu linux setup guide

Upgrade to Oracle Database 19c UNIX

You can access the complete Oracle documentation as a member of the Oracle Technology Network (OTN). 2. Check the Oracle-recommended kernel parameters and shell limits for your operating system with Oracle Database 19c. The kernel parameters and shell limits are also checked in detail during the software

  Network, Kernel

End to End Learning for Self-Driving Cars - Nvidia

We trained a convolutional neural network (CNN) to map raw pixels from a sin-gle front-facing camera directly to steering commands. This end-to-end approach ... 3x3 kernel 3x3 kernel 5x5 kernel 5x5 kernel 5x5 kernel Flatten Figure 4: CNN architecture. The network has about 27 million connections and 250 thousand

  Network, Driving, Kernel, End to end

Selective Kernel Networks - CVF Open Access

network termed Selective Kernel Networks (SKNets). On the ImageNet and CIFAR benchmarks, we empirically show that SKNet outperforms the existing state-of-the-art archi-tectures with lower model complexity. Detailed analyses show that the neurons in SKNet can capture target objects

  Network, Kernel

Linux Network Administrators Guide

Table of Contents 1. Purpose and Audience for This Book.....1

  Network