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

CHAOS THEORY AND ITS APPLICATIONS IN OUR REAL LIFE

published a book, The Fractal Geometry of Nature (Devaney and Keen 1989), which looked into a mathematical basis of pattern formation in nature, much like the earlier work of Turing. His fractals (the geometry of fractional dimensions) helped describe or picture the actions of chaos, rather than explain it. Chaos and its workings could now be

  Geometry, Fractal, Fractal geometry

Quiz #10 (Form A) KEY ANSWERS ARE IN BOLD ITALICS. b) 34

7) A fractal is an object a) that reveals new features at smaller scales b) that is symmetric c) that has proportional features d) whose length to width ratio is the golden mean 8) Which of the following is NOT an example of fractals? a) the number of spirals on a pineapple b) coastlines c) ferns d) coral 9) Fractal geometry has applications in

  Geometry, Fractal, Fractal geometry

Application of Mathematics in civil Engineering

Jun 11, 2017 · Fractal Geometry (the newest branch of Geometry) is used by Civil Engineers to analyze such entities as the friction between objects, the clumping of materials, and the porosity of soils, all of which involve geometric patterns that repeat on an ever decreasing scale.

  Engineering, Civil, Geometry, Fractal, Civil engineering, Fractal geometry

A First Course in Complex Analysis - Mathematics

fractal-coloring method. A subset of the image is seen as a standard Sierpinski triangle. The chaos game was also re-purposed to create the foreground image. Robert Cha˙er is Professor Emeritus at Central Michigan University. His academic interests are in abstract algebra, combinatorics, geometry, and computer applications. Since retirement

  Analysis, Complex, Geometry, Fractal, Complex analysis

Experiments with MATLAB - MathWorks

6 Fractal Fern 75 7 Google PageRank 83 8 Exponential Function 97 9 T Puzzle 113 10 Magic Squares 123 11 TicTacToe Magic 141 12 Game of Life 151 13 Mandelbrot Set 163 ... in geometry, algebra, and trigonometry. We will introduce ideas from calculus, matrix theory, and ordinary differential equations, but we do not assume that you ...

  Geometry, Fractal

Maths Investigation Ideas for A-level, IB and Gifted GCSE ...

6) Mandelbrot set and fractal shapes : Explore the world of infinitely generated pictures and fractional dimensions. 7) Sierpinksi triangle : a fractal design that continues forever. 8) Squaring the circle : This is a puzzle from ancient times - which was to find out whether a square could be created that had the same area as a given circle.

  Fractal