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

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.

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

06 Random Number Generation - fu-berlin.de

6.7 Pseudo-Random Numbers • Goal: To produce a sequence of numbers in [0,1] that simulates, or imitates, the ideal properties of random numbers (RN). Prof. Dr. Mesut Güneş Ch. 6 Random-Number Generation

  Generation, Number, Random, Random number generation

STM32 microcontroller random number generation …

STM32 microcontroller random number generation validation using the NIST statistical test suite Introduction Many standards created requirements and references for the construction, the validation and the use of random number generators (RNGs), in order to verify that the output they produce is indeed random.

  Generation, Number, Random, Random number generation, Random number

Hand-book on STATISTICAL DISTRIBUTIONS for experimentalists

6.7 Random Number Generation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 7 Cauchy Distribution 26 7.1 Introduction ...

  Generation, Number, Random, Random number generation

Random Number Generation C++

Random Number Generation C++ It is often useful to generate random numbers to produce simulations or games (or homework problems :) One way to generate these numbers in C++ is to use the function rand(). Rand is defined as: #include <cstdlib> int rand();

  Generation, Number, Random, Random number generation c

Generation of Computers - LSP4YOU

1.1 GENERATION OF COMPUTERS ... Magnetic ferrite core memories were used as main memory which is a random-access nonvolatile memory ... Generation number beyond IV, have been used occasionally to describe some current computer system that have a dominant organizational or application driven feature.

  Generation, Number, Random, Generation number

Understanding deep learning requires rethinking …

pletely unstructured random noise. We corroborate these experimental ndings with a theoretical construction showing that simple depth two neural networks al-ready have perfect nite sample expressivity as soon as the number of parameters exceeds the number of data points as it usually does in practice.

  Learning, Number, Deep, Random, Requires, Deep learning requires

STM32F415xx STM32F417xx - STMicroelectronics

• True random number generator • CRC calculation unit • 96-bit unique ID • RTC: subsecond accuracy, hardware calendar Table 1. Device summary Reference Part number STM32F415xx STM32F415RG, STM32F415VG, STM32F415ZG, STM32F415OG STM32F417xx STM32F417VG, STM32F417IG, STM32F417ZG, STM32F417VE, STM32F417ZE, STM32F417IE

  Number, Random, Random number

Experiment 6: Frequency Modulation (FM), Generation and ...

2.0 FM Generation FM generation is quite easy. All that is needed is a linear voltage-to-frequency converter. In EECS 311 and EECS 413, you will study in detail amplifiers, feedback and oscillators. If a voltage-controlled capacitor is placed in the

  Generation

LTC6803-2/LTC6803-4 - Multicell Battery Stack Monitor

The LTC ®6803 is a 2nd generation, complete battery moni - toring IC that includes a 12-bit ADC, a precision voltage reference, a high voltage input multiplexer and a serial interface. Each LTC6803 can measure up to 12 series connected battery cells or supercapacitors. Many LTC6803 devices can be stacked to measure the voltage of each cell

  Generation