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

Memory and Aging - American Psychological Association

Memory and Aging Possible Causes of Memory Problems If you or a loved one is having memory problems that are more bothersome than you would normally expect, don’t assume that Alzheimer’s or another form of dementia is the culprit. Glitches in memory can be caused by many physical and psychological conditions that are reversible.

  American, Memory, Problem, Association, Psychological, American psychological association, Memory problems

Linked List Problems - Stanford University

problems and work through the solutions. Linked lists are well-suited for memory drawings, so these problems are an excellent opportunity to develop your visualization skill. The problems in this document use regular linked lists, without simplifcations like dummy headers. Dedication This Jan-2002 revision includes many small edits.

  Memory, Lists, Problem, Linked, Linked list problems

A New Approach to Linear Filtering and Prediction Problems

method, a single derivation covers a large variety of problems: growing and infinite memory filters, stationary and nonstationary statistics, etc.; difficulty (3) disappears. Having guessed the “state” of the estimation (i.e., filtering or prediction) problem correctly, one is led to a nonlinear difference (or differential)

  Memory, Problem

What is Direct Memory Access (DMA) and Why Should We …

copies of recently accessed memory areas are in cache if device writes to memory, cache area is invalidated so it will have to be paged in If device reads data from memory, cache flushed out first • Generic DMA layer ensures all of above are not a problem over many architectures provided some rules are obeyed Allocating a DMA Buffer

  What, Memory, Direct, Access, What is direct memory access

How Words Cast Their Spell - AFT

ment of visual memory for whole words.5 More recent studies, however, do not support the notion that visual memory is the key to good spelling.6 Several researchers have found that rote visual memory for letter strings is limited to two or three letters in a word.7 In addition, studies of the errors

  Memory

Aurora R6 Setup and Specifications

Memory Table 5. Memory specifications Slots Four DIMM sockets Type DDR4 Speed • 2400 MHz • Up to HyperX FURY DDR4 XMP at 2667 MHz Configurations supported Per slot 4 GB, 8 GB, and 16 GB Total memory 4 GB, 8 GB, 16 GB, 32 GB, and 64 GB (2400 MHz only) 12

  Memory

Generating Sequences With Recurrent Neural Networks

memory is a more profound and e ective solution. Long Short-term Memory (LSTM) [16] is an RNN architecture designed to be better at storing and accessing information than standard RNNs. LSTM has recently given state-of-the-art results in a variety of sequence processing tasks, including speech and handwriting recognition [10, 12]. The main goal ...

  Memory, With, Sequence, Generating, Neural, Recurrent, Generating sequences with recurrent neural

Learning: Theory and Research

Cognitive Science: Memory and Learning Anthropology: Situated Learning in Communities of Practice Psychology: Motivation and Learning Education: Organizing the Learning Process Education: Learning to Think in a Discipline GSI Teaching & Resource Center gsi@berkeley.edu 510-642-4456 301 Sproul Hall Office Hours 9–12, 1–4

  Memory

Analyzing and Mitigating Data Stalls in DNN Training

of data that can be cached in memory, the number of CPU threads used to fetch and pre-process data, and GPU generation. We then analyze how these factors affect the data pipeline and DNN training. Our analysis shows that data stalls are prominent in popular com-puter vision and audio DNNs, as opposed to text-based NLP models.

  Memory