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

Fundamentals of Deep Learning - Nvidia

> Improve training speed by combining transfer learning and feature extraction. > Discuss advanced neural network architectures and recent areas of research where students can further improve their skills. Final Review (15 mins) > Review key learnings and answer questions. > Complete the assessment and earn a certificate. > Complete the ...

  Feature, Nvidia, Learning, Fundamentals, Deep, Extraction, And feature extraction, Fundamentals of deep learning

Rich Feature Hierarchies for Accurate Object Detection and ...

Feature extraction. We extract a 4096-dimensional fea-ture vector from each region proposal using the Caffe [21] implementation of the CNN described by Krizhevsky et al. [22]. Features are computed by forward propagating a mean-subtracted 227 227 RGB image through five con-volutional layers and two fully connected layers. We refer

  Feature, True, Extraction, Feature extraction, Fea ture

Classification of Fingerprints - Michigan State University

coefficients form the feature for classification. Fingerprint as Oriented Texture (a) Ridges in local region (b) Fourier spectrum of (a) ... Singularity Extraction in Fingerprint Images”, IEEE Transactions on Image Processing, October 2004. Generation of Orientation Field Flow Curves (OFFCs) Continuous

  Feature, Extraction

Independent Component Analysis: Algorithms and Applications

Feature extraction by ICA will be explained in more detail later on. All of the applications described above can actually be formulated in a unified mathematical framework, that of ICA. This is a very general-purpose method of signal processing and data analysis.

  Feature, Extraction, Feature extraction

Sustainable Finance Disclosure Regulation - Article 8 ...

assets involved in the extraction, storage, transport or manufacture of fossil fuels New Exposure to energy-inefficient real estate assets Share of investments in energy inefficient real estate assets Previously mandatory indicator for all funds and now only real estate

  Finance, Regulations, Sustainable, Article, Disclosures, Extraction, Sustainable finance disclosure regulation article

Mining of Massive Datasets - Stanford University

iv PREFACE 7. Two key problems for Web applications: managing advertising and rec-ommendation systems. 8. Algorithms for analyzing and mining the structure of very large graphs,

  Mining, Massive, Dataset, Massive datasets

-デジタル画像と定量化- - Riken

1 吉澤信 shin@riken.jp, 非常勤講師-デジタル画像と定量化-その5：領域抽出・ラべリング・細線化 九州大学大学院