arXiv:0706.3639v1 [cs.AI] 25 Jun 2007

arXiv:0706.3639v1 [cs.AI] 25 Jun 2007 Technical Report IDSIA-07-07 A Collection of Definitions of Intelligence Shane Legg IDSIA, Galleria …

  Intelligence, Collection

Deep Residual Learning for Image Recognition - …

Deep Residual Learning for Image Recognition Kaiming He Xiangyu Zhang Shaoqing Ren Jian Sun Microsoft Research fkahe, v-xiangz, v-shren, jiansung@microsoft.com

  Image, Learning, Residual, Recognition, Residual learning for image recognition

arXiv:1301.3781v3 [cs.CL] 7 Sep 2013

For all the following models, the training complexity is proportional to O = E T Q; (1) where E is number of the training epochs, T is the number of …

@google.com arXiv:1609.03499v2 [cs.SD] 19 Sep 2016

where 1 <x t <1 and = 255. This non-linear quantization produces a significantly better reconstruction than a simple linear quantization scheme. …

A Tutorial on UAVs for Wireless Networks: …

A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems Mohammad Mozaffari 1, ... to UAVs in wireless communications is the work in …

  Network, Communication, Wireless, Wireless communications, Wireless networks

Adversarial Generative Nets: Neural Network …

Adversarial Generative Nets: Neural Network Attacks on State-of-the-Art Face Recognition Mahmood Sharif, Sruti Bhagavatula, Lujo Bauer Carnegie Mellon University

  Network, Attacks, Nets, Adversarial generative nets, Adversarial, Generative, Neural network, Neural, Neural network attacks

Massive Exploration of Neural Machine Translation ...

Massive Exploration of Neural Machine Translation Architectures Denny Britzy, Anna Goldie, Minh-Thang Luong, Quoc Le fdennybritz,agoldie,thangluong,qvlg@google.com Google Brain

  Architecture, Machine, Exploration, Translation, Neural, Exploration of neural machine translation, Exploration of neural machine translation architectures

Mastering Chess and Shogi by Self-Play with a …

Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm David Silver, 1Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, 1Matthew Lai, Arthur Guez, Marc Lanctot,1

Going deeper with convolutions - arXiv

Going deeper with convolutions Christian Szegedy Google Inc. Wei Liu University of North Carolina, Chapel Hill Yangqing Jia Google Inc. Pierre Sermanet

  With, Going, Going deeper with convolutions, Deeper, Convolutions

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

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications Andrew G. Howard Menglong Zhu Bo Chen Dmitry Kalenichenko Weijun Wang Tobias Weyand Marco Andreetto Hartwig Adam

  Applications

Lecture 9: CNN Architectures

ImageNet Large Scale Visual Recognition Challenge (ILSVRC) winners First CNN-based winner. Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 9 - 23 May 2, 2017 ImageNet Large Scale Visual Recognition Challenge (ILSVRC) winners ZFNet: …

  Large, Scale, Visual, Recognition, Imagenet, Imagenet large scale visual recognition

ImageNet Classification with Deep Convolutional Neural ...

Challenge, an annual competition called the ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) has been held. ILSVRC uses a subset of ImageNet with roughly 1000 images in each of 1000 categories. In all, there are roughly 1.2 million training images, 50,000 validation images, and

  With, Large, Scale, Classification, Visual, Deep, Recognition, Convolutional, Imagenet, Imagenet large scale visual recognition, Imagenet classification with deep convolutional

Classification of Trash for Recyclability Status

AlexNet [1], which won the 2012 ImageNet Large-Scale Visual Recognition Challenge (ILSVRC). The architecture is relatively simple and not extremely deep, and is, of course, known to perform well. AlexNet was influential because it started a trend of CNN approaches being very popular in the Im-ageNet challenge and becoming the state of the art

  Large, Scale, Visual, Recognition, Imagenet, Agente, A meeting, Imagenet large scale visual recognition

Microsoft COCO: Common Objects in Context

Microsoft COCO: Common Objects in Context Tsung-Yi Lin 1, Michael Maire2, Serge Belongie , James Hays3, Pietro Perona2, Deva Ramanan4, Piotr Doll ar 5, C. Lawrence Zitnick 1Cornell, 2Caltech, 3Brown, 4UC Irvine, 5Microsoft Research Abstract. We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object

  Microsoft, Context, Common, Recognition, Object, Coco, Microsoft coco, Common objects in context

Video Swin Transformer

model pre-trained on a large-scale image dataset. With a model pre-trained on ImageNet-21K, we interestingly find that the learning rate of the backbone architecture needs to be smaller (e.g. 0.1 ) than that of the head, which is randomly initialized. As a …

  Large, Scale, Imagenet

Dense Contrastive Learning for Self-Supervised Visual Pre ...

labeling, making it hard to collect data at a massive scale to pre-train a universal feature representation. Recently, unsupervised visual pre-training has attracted much research attention, which aims to learn a proper vi-sual representation from a large set of unlabeled images. A few methods [17, 2, 3, 14] show the effectiveness in down-

  Large, Scale, Visual, Usal, Vi sual

Quo Vadis, Action Recognition? A New Model and the ...

ImageNet. In this paper we demonstrate that video models are best pre-trained on videos and report significant improvements by using spatio-temporal classifiers pre-trained on Kinetics, a freshly collected, large, challenging human action video dataset. mentation, depth prediction, pose estimation, action classi-fication.

  Large, Recognition, Imagenet

ImageNet: A Large-Scale Hierarchical Image Database

show that ImageNet is a large-scale, accurate and diverse image database (Section2). In Section4, we present a few simple application examples by exploiting the current Ima-geNet, mostly the mammal and vehicle subtrees. Our goal is to show that ImageNet can serve as a useful resource for visual recognition applications such as object recognition,

  Large, Scale, Visual, Recognition, Imagenet, Gentes, A meeting, Visual recognition