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

第一讲政府治理与事权划分 - econ.fudan.edu.cn

2018年初挤水分 ⚫ 2017年辽宁省两会，省长陈求发：“我们顶着面子上难看的压力， 认真地挤压水分，2015年夯实了财政收据，2016年以来努力夯实其 他经济数据” ⚫ 2018年内蒙古核减2016年规模以上工业增加值2900亿元，占全部工 业增加值的40%。

  2018, Fudan

The Chinese Pension System - Pension Research Council

Sep 12, 2018 · Fudan University September 12, 2018 * This paper is written for Handbook on Chinese Financial Markets, edited by Marlene Armstad, Guofeng Sun and Wei Xiong. We are grateful to the Editors for guidance, and for the conference participants in Shenzhen Institute of Finance for useful comments. We are responsible for the remaining errors.

  System, 2018, Chinese, Pension, Fudan, The chinese pension system

兰小欢简历 - econ.fudan.edu.cn

2018：上海纽约大学；香港中文大学深圳分校 2017：西安交通大学；暨南大学；华南师范大学；香港中文大学深圳分校；东亚五校会议（日 本东京庆应义塾大学）；国际公共财政学会年会（东 …

  2018, Fudan

arXiv:2106.07139v2 [cs.AI] 15 Jun 2021

mud et al.,2016;Peters et al.,2018;Howard and Ruder,2018), yet the performance of these models is still limited by their model size and depth. With the development of deep neural networks in the NLP community, the introduction of Trans-formers (Vaswani et al.,2017) makes it feasible to train very deep neural models for NLP tasks. With

  2018

寻找合适的环境规制强度指标 - hbue.edu.cn

献将总量控制政策作为环境规制强度变化的标志予以考察（Shi&Xu，2018），但排污费在这方 面的重要性仍然缺乏关注。①现有指标存在着同时性、多维度性、测量误差等问题，并且较为 侧重企业行为的度量，忽视政府的主体行为。

  2018