Selective Kernel Networks - CVF Open Access

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

arXiv:1512.00567v3 [cs.CV] 11 Dec 2015

cused on finding higher performing convolutional neural networks. Starting in 2014, the quality of network architec-tures significantly improved by utilizing deeper and wider networks. VGGNet [18] and GoogLeNet [20] yielded simi-larly high performance in the 2014 ILSVRC [16] classifica-tion challenge. One interesting observation was that gains

  High, Network, Neural network, Neural

Abstract arXiv:1611.05431v2 [cs.CV] 11 Apr 2017

parameters, and depth is exposed as an essential dimension in neural networks. Moreover, we argue that the simplicity of this rule may reduce the risk of over-adapting the hyper-parameters to a specific dataset. The robustness of VGG-nets and ResNets has been proven by various visual recog-nition tasks [7,10,9,28,31,14] and by non-visual tasks

  Network, Dimensions, Neural network, Neural

Andrew G. Howard Menglong Zhu Bo Chen Dmitry ... - arXiv

cient neural networks in the recent literature, e.g. [16,34, 12,36,22]. Many different approaches can be generally categorized into either compressing pretrained networks or training small networks directly. This paper proposes a class of network architectures that allows a model devel-oper to specifically choose a small network that matches

  Network, Neural network, Neural

Self-Supervised Learning

•Goal: represent words as vectors for input into neural networks. •One-hot vectors? (single 1, rest 0s) pizza = [0 0 0 0 0 1 0 … 0 0 0 0 0 ] pie = [0 0 0 0 0 0 0 … 0 0 0 1 0 ] ☹Millions of words high-dimensional, sparse vectors ☹No notion of word similarity •Instead: we want a dense, low-dimensional vector for each word such that ...

  High, Network, Neural network, Neural

Frequency Principle: Fourier Analysis Sheds Light on Deep ...

We study the training process of Deep Neural Networks (DNNs) from the Fourier analysis perspective. We demonstrate a very universal Frequency Principle (F-Principle) — DNNs often fit target functions from low to high frequencies — on high-dimensional benchmark datasets such as MNIST/CIFAR10 and deep neural net-works such as VGG16.

  High, Network, Work, Neural network, Neural, Neural net works

Support-vector networks - Springer

With this extension we consider the support-vector networks as a new class of learning machine, as powerful and universal as neural networks. In Section 5 we will demonstrate how well it generalizes for high degree polynomial decision surfaces (up to order 7) in a high dimensional space (dimension 256).

  High, Network, Support, Dimensions, Vector, Neural network, Neural, For high, Support vector networks

ECA-Net: Efficient Channel Attention for Deep ...

Deep convolutional neural networks (CNNs) have been widely used in computer vision community, and have ∗Qinghua Hu is the corresponding author. Email: {qlwang, wubanggu, huqinghua}@tju.edu.cn. The work was sup-ported by the National Natural Science Foundation of China (Grant No. 61806140, 61876127, 61925602, 61971086, U19A2073, 61732011), Ma-

  Network, Neural network, Neural

Going Deeper With Convolutions - cv-foundation.org

3. Motivation and High Level Considerations The most straightforward way of improving the perfor-mance of deep neural networks is by increasing their size. This includes both increasing the depth – the number of net-Figure 1: Two distinct classes from the 1000 classes of the ILSVRC 2014 classification challenge. Domain knowledge is re-

  High, Network, Neural network, Neural