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

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

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

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

BENGALURU CITY UNIYERSITY

Jan 04, 2022 · Arrays: Definition, Linear arrays, arrays as ADT, Representation of Linear Arrays in Memory,Traversing Linear arrays, Inserting and deleting, Multi-dimensional arrays, Matrices and Sparse matrices. UNIT-II [12 Hours] Linked list: Definition, Representation of Singly Linked List in memory,Traversing a Singly linked list,

  Unit, Representation, Arsesp

Multimodal Deep Learning - ai.stanford.edu

tive shared representation learning. 1. Introduction In speech recognition, humans are known to inte-grate audio-visual information in order to understand speech. This was rst exempli ed in the McGurk ef-fect (McGurk & MacDonald,1976) where a visual /ga/ with a voiced /ba/ is perceived as /da/ by most sub-jects.

  Learning, Representation, Deep, Recognition, Multimodal, Multimodal deep learning

Michael D. Moffitt Qi Song, Jie Li, Chenghong Li, Hao Guo ...

517: SCIR-Net: Structured Color Image Representation Based 3D Object Detection Network from Point Clouds Qingdong He, Hao Zeng, Yi Zeng, Yijun Liu 521: Memory-Based Jitter: Improving Visual Recognition on Long-Tailed Data with Diversity In …

  Representation, Recognition

fzhangxiangyu,zxy,linmengxiao,sunjiang@megvii.com arXiv ...

unit [9] in Fig2(a). It is a residual block. In its residual branch, for the 3 3 layer, we apply a computational eco-nomical 3 3 depthwise convolution [3] on the bottleneck feature map. Then, we replace the first 1 1 layer with pointwise group convolution followed by a channel shuffle operation, to form a ShuffleNet unit, as shown in Fig2(b).

  Unit

arXiv:1408.5882v2 [cs.CL] 3 Sep 2014

sparse, 1-of-V encoding (here V is the vocabulary size) onto a lower dimensional vector space via a hidden layer, are essentially feature extractors that encode semantic features of words in their dimen-sions. In such dense representations, semantically close words are likewise close—in euclidean or cosine distance—in the lower dimensional ...

  Arsesp

Sparse autoencoder - Stanford University

Sparse autoencoder 1 Introduction Supervised learning is one of the most powerful tools of AI, and has led to automatic zip code recognition, speech recognition, self-driving cars, and a continually improving understanding of the human genome. Despite its sig-nificant successes, supervised learning today is still severely limited. Specifi-

  Recognition, Arsesp, Autoencoder, Sparse autoencoder

Recti er Nonlinearities Improve Neural Network Acoustic Models

hidden unit’s activation h(i) is given by, h(i) = ˙(w(i)T x); (1) where ˙( ) is the tanh function, w(i) is the weight vec-tor for the ith hidden unit, and xis the input. The input is speech features in the rst hidden layer, and hidden activations from the previous layer in deeper layers of the DNN.

  Unit

Introduction to Deep Learning - Stanford University

Convolutional Networks for Large-Scale Image Recognition. Its main contribution was in showing that the depth of the network is a critical component for good performance. Their final best network contains 16 CONV/FC layers and, appealingly, features an extremely homogeneous architecture that only performs 3x3 convolutions and 2x2

  Recognition