ImageNet Classification with Deep Convolutional Neural Networks Alex Krizhevsky University of Toronto kriz@cs.utoronto.ca Ilya Sutskever University of Toronto ilya@cs.utoronto.ca Geoffrey E. Hinton University of Toronto hinton@cs.utoronto.ca Abstract We trained a large, deep convolutional neural network to classify the 1.2 million Save PDF. /Resources 101 0 R /Resources 95 0 R stream ImageNet Classification with Deep Convolutional Neural Networks 摘要 我们训练了一个大型深度卷积神经网络来将ImageNet LSVRC-2010数据集中的120万张高清图片分到1000个不同的类别中。在测试数据中,我们将Top-1错误 Using very deep autoencoders for content-based image retrieval. 我们训练了一个庞大的深层卷积神经网络,将ImageNet LSVRC-2010比赛中的120万张高分辨率图像分为1000个不同的类别。在测试数据上,我们取得了37.5%和17.0%的前1和前5的错误率,这比以前的先进水平要好得多。具有6000万个参数和650,000个神经元的神经网络由五个卷积层组成,其中一些随后是最大池化层,三个全连接层以及最后的1000个softmax输出。为了加快训练速度,我们使用非饱和神经元和能高效进行卷积运算的GPU实现。为了减少全连接层中的过拟合,我们采用了最近开发的称为“dropout” … ImageNet은 22,000개의 범주를 가진 1,500만개 이상의 라벨링된 고해상도 이미지 셋이다. /Publisher (Curran Associates\054 Inc\056) 11 0 obj endobj /Filter /FlateDecode Some of the most important innovations have sprung from submissions by academics and industry leaders to the ImageNet Large Scale Visual Recognition Challenge, or ILSVRC. << /Language (en\055US) L. Fei-Fei, R. Fergus, and P. Perona. B.C. Abstract We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. 一、基本信息标题:ImageNet Classification with Deep Convolutional Neural Networks时间:2012出版源:Neural Information Processing Systems (NIPS)论文领域:深度学习,计算机视觉引用格式:Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks… Howard, W. Hubbard, L.D. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. Convolutional networks can learn to generate affinity graphs for image segmentation. Check if you have access through your login credentials or your institution to get full access on this article. Simard, D. Steinkraus, and J.C. Platt. /Parent 1 0 R /Created (2012) 5 0 obj To reduce overriding in the fully-connected layers we employed a recently-developed regularization method called "dropout" that proved to be very effective. NeurIPS 2012 • Alex Krizhevsky • Ilya Sutskever • Geoffrey E. Hinton. We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. /MediaBox [ 0 0 612 792 ] /Type (Conference Proceedings) N. Pinto, D.D. >> /ModDate (D\07220140423102144\05507\04700\047) But this was not possible just a decade ago. Seung. Non-image Data Classification with Convolutional Neural Networks. ImageNet Classification with Deep Convolutional Neural Networks - paniabhisek/AlexNet /Type /Page Large scale visual recognition challenge 2010. www.image-net.org/challenges. In computer vision, a particular type of DNN, known as Convolutional Neural1, 2, 3 In, Y. J. Deng, A. Berg, S. Satheesh, H. Su, A. Khosla, and L. Fei-Fei. In this paper we compare performance of different regularization techniques on ImageNet Large Scale Visual Recognition Challenge 2013. 2012. Metric Learning for Large Scale Image Classification: Generalizing to New Classes at Near-Zero Cost. Le Cun, B. Boser, J.S. 6 0 obj 6, Pages 84-90 10.1145/3065386. It helped show that artificial neural networks weren’t doomed as they were thought to be and sparked the beginning of the cutting-edge research happening in deep learning all over the world! Jackel, et al. We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into … /Subject (Neural Information Processing Systems http\072\057\057nips\056cc\057) Denker, D. Henderson, R.E. Russell, A. Torralba, K.P. /Type /Page << On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. ImageNet Classification with Deep Convolutional Neural Networks. /Parent 1 0 R A. Krizhevsky. 4 0 obj Original paper: Imagenet Classification with Deep Convolutional Neural Networks Best practices for convolutional neural networks applied to visual document analysis. ImageNet Classification with Deep Convolutional Neural Networks Apr 9, 2017 in CV 1. Bell and Y. Koren. /Parent 1 0 R The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. Visualizing and Understanding Convolutional Networks, 2013. Multi-column deep neural networks for image classification. 2012 Like the large-vocabulary speech recognition paper we looked at yesterday, today’s paper has also been described as a landmark paper in the history of deep learning. 8 0 obj endobj Deep Neural Networks (DNNs) are powerful models that have achieved excel-lent performance on difficult learning tasks. /Contents 28 0 R /Resources 39 0 R On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. In. endobj Gambardella, and J. Schmidhuber. ImageNet Classification with Deep DOI:10.1145/3065386 Convolutional Neural Networks By Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton Abstract We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. N. Pinto, D. Doukhan, J.J. DiCarlo, and D.D. ImageNet Classification with Deep Convolutional Neural Networks Deep Convolutional Neural Netwworks로 ImageNet 분류 초록 ImageNet NSVRC-2010 대회의 1.2 million 고해상도 이미지를 1000개의 서로 다른 클래스로 분류하기 ImageNet Classification with Deep Convolutional Neural Networks Alex Krizhevsky University of Toronto kriz@cs.utoronto.ca Ilya Sutskever University of Toronto Title ImageNet Classification with Deep Convolutional Neural Networks Its ability to extract and https://dl.acm.org/doi/10.5555/2999134.2999257. Cox, and J.J. DiCarlo. >> In, H. Lee, R. Grosse, R. Ranganath, and A.Y. >> G.E. ImageNet Classification with Deep Convolutional Neural Networks ... A Krizhevsky , I Sutskever , G Hinton. >> 12 0 obj This paper was a breakthrough in the field of computer vision. J. Sanchez and F. Perronnin. /Contents 65 0 R ImageNet Classification with Deep Convolutional Neural Networks Authors: Alex Krizhevsky, Ilya Sutskever, Geoffrey Hinton University of Toronto Presenter: Yuanzhe Li Hinton. Paper Explanation : ImageNet Classification with Deep Convolutional Neural Networks (AlexNet) Posted on June 6, 2018 June 28, 2018 by natsu6767 in Deep Learning ILSVRC-2010 test images and the five labels considered most probable by the model. Very Deep Convolutional Networks for Large-Scale . ∙ University of Canberra ∙ 11 ∙ share . 3 0 obj Lessons from the netflix prize challenge. The Convolutional Neural Networks (CNN) techniques have the potency to accomplish image classification for a variety of datasets. Technical Report 7694, California Institute of Technology, 2007. /Type /Catalog In. However there is no clear understanding of why they perform so well, or how they might be improved. Large and Deep Convolutional Neural Networks achieve good results in image classification tasks, but they need methods to prevent overfitting. /Type /Page >> The proposed model is based on deep convolutional neural networks. ImageNet Classification with Deep Convolutional Neural Networks A. Krizhevsky , I. Sutskever , and G. Hinton . Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. /Editors (F\056 Pereira and C\056J\056C\056 Burges and L\056 Bottou and K\056Q\056 Weinberger) A high-throughput screening approach to discovering good forms of biologically inspired visual representation. A. Krizhevsky. ImageNet Classification with Deep Convolutional Neural Networks ImageNet Classification with Deep Convolutional Neural Networks. /Contents 71 0 R /Type /Page Convolutional Neural Networks (CNNs) is one of the most popular algorithms for deep learning which is mostly used for image classification, natural language processing, and time series forecasting. Freeman. 7 0 obj We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. /Date (2012) /Author (Alex Krizhevsky\054 Ilya Sutskever\054 Geoffrey E\056 Hinton) endobj Large Convolutional Network models have recently demon-strated impressive classification performance on the ImageNet bench-mark Krizhevsky et al. They used two GPU, and spread the net across them, implementing parallelization scheme, they put half of the neurons on each GPU, but the GPU will only communicate in … /Parent 1 0 R Main idea Architecture ... Convolutional neural networks Output Hidden Data ∙ UNIVERSITY OF TORONTO ∙ 8 ∙ share … ImageNet Classification with Deep Convolutional Neural Networks ... Communications of the ACM, Vol. It helps the marine biologists to have greater understanding of the fish species and their habitats. /Description-Abstract (We trained a large\054 deep convolutional neural network to classify the 1\0563 million high\055resolution images in the LSVRC\0552010 ImageNet training set into the 1000 different classes\056 On the test data\054 we achieved top\0551 and top\0555 error rates of 39\0567\134\045 and 18\0569\134\045 which is considerably better than the previous state\055of\055the\055art results\056 The neural network\054 which has 60 million parameters and 500\054000 neurons\054 consists of five convolutional layers\054 some of which are followed by max\055pooling layers\054 and two globally connected layers with a final 1000\055way softmax\056 To make training faster\054 we used non\055saturating neurons and a very efficient GPU implementation of convolutional nets\056 To reduce overfitting in the globally connected layers we employed a new regularization method that proved to be very effective\056) ImageNet Classification with Deep Convolutional Neural Networks 摘要 我们训练了一个大型深度卷积神经网络来将ImageNet LSVRC-2010竞赛的120万高分辨率的图像分到1000不同的类别中。在测试数据上,我们得到了top-1 37.5%, top-5 17.0%的错误率,这个结果比目前的最好结果好很多。 ImageNet Classification with Deep Deep Convolutional Convolutional Neural Neural Networks Alex Alex KrizhevskyKrizhevsky, IlyaIlyaSutskeverSutskever, Geoffrey E. Hinton, Geoffrey E. Hinton In, Y. LeCun, F.J. Huang, and L. Bottou. /Length 3020 2016/2017 Learning multiple layers of features from tiny images. /Type /Pages 실험에서는 ImageNet의 서브셋을 사용했고, 120만장의 학습 이미지, 5만장의 검증 이미지, 15만장의 테스트 이미지로 이루어져 있다. ImageNet Classification with Deep Convolutional Neural Networks 摘要. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is … It’s also a surprisingly easy read! Murphy, and W.T. All Holdings within the ACM Digital Library. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R.R. /Published (2012) What is the best multi-stage architecture for object recognition? We trained a large, deep convolutional neural network to classify the 1.3 million high-resolution images in the LSVRC-2010 ImageNet training set into the 1000 different classes. BibTeX @INPROCEEDINGS{Krizhevsky_imagenetclassification, author = {Alex Krizhevsky and Ilya Sutskever and Geoffrey E. Hinton}, title = {Imagenet classification with deep convolutional neural networks}, booktitle = {Advances in Neural Information Processing Systems}, year = {}, pages = {2012}} /Contents 100 0 R Why is real-world visual object recognition hard? << ImageNet Classification with Deep Convolutional Neural Networks Alex Krizhevsky Ilya Sutskever Geoffrey Hinton University of Toronto Canada Paper with same name to appear in NIPS 2012. 07/07/2020 ∙ by Anuraganand Sharma, et al. >> /Resources 29 0 R S.C. Turaga, J.F. CS 8803 DL (Deep learning for Pe) Academic year. >> ImageNet Classification with Deep Convolutional Neural Networks By Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton Communications of the ACM, June 2017, Vol. In this paper, we presented an automated system for identification and classification of fish species. 摘要: We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif- ferent classes. >> A. Krizhevsky and G.E. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is … Published Date: 12. High-dimensional signature compression for large-scale image classification. #ai #research #alexnetAlexNet was the start of the deep learning revolution. /MediaBox [ 0 0 612 792 ] Like the large-vocabulary speech recognition paper we looked at yesterday, today’s paper has also been described as a landmark paper in the history of deep learning. The ACM Digital Library is published by the Association for Computing Machinery. D.C. Cireşan, U. Meier, J. Masci, L.M. <<

We trained a large, deep convolutional neural network to classify the 1.3 million high-resolution images in the LSVRC-2010 ImageNet training set into the 1000 different classes. Master's thesis, Department of Computer Science, University of Toronto, 2009. Convolutional Neural Networks (CNNs) is one of the most popular algorithms for deep learning which is mostly used for image classification, natural language processing, and time series forecasting. %PDF-1.3 endobj /Resources 72 0 R /Book (Advances in Neural Information Processing Systems 25) /MediaBox [ 0 0 612 792 ] 4824-imagenet-classification-with-deep-convolutional-neural-networks ImageNet Classification with Deep Deep Convolutional Convolutional Neural Neural Networks Alex Alex KrizhevskyKrizhevsky, IlyaIlyaSutskeverSutskever, Geoffrey E. Hinton, Geoffrey E. Hinton Database ImageNet 15M images 22K Salakhutdinov. endobj /Parent 1 0 R Labelme: a database and web-based tool for image annotation. Convolutional deep belief networks on cifar-10. [18]. /Description (Paper accepted and presented at the Neural Information Processing Systems Conference \050http\072\057\057nips\056cc\057\051) The rise in popularity and use of deep learning neural network techniques can be traced back to the innovations in the application of convolutional neural networks to image classification tasks. Georgia Institute of Technology. ImageNet Classification with Deep Convolutional Neural Networks Alex Krizhevsky University of Toronto kriz@cs.utoronto.ca Ilya Sutskever University of Toronto ilya@cs.utoronto.ca Geoffrey E. Hinton University of Toronto hinton@cs IMAGENet Classification輪_ with Deep Convolutional Neural Networks講: NIPS ‘12 2012 / 12 / 20 本位田研究室 M1 堀内 新吾 2. J. Deng, W. Dong, R. Socher, L.-J. >> We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classe A. Berg, J. Deng, and L. Fei-Fei. ImageNet: A Large-Scale Hierarchical Image Database. /MediaBox [ 0 0 612 792 ] In, Y. LeCun, K. Kavukcuoglu, and C. Farabet. Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories. /Resources 14 0 R Although DNNs work well whenever large labeled training sets are available, they cannot be used to map NIPS'12: Proceedings of the 25th International Conference on Neural Information Processing Systems - Volume 1. /MediaBox [ 0 0 612 792 ] Imagenet classification with deep convolutional neutral networks ImageNet Classification with Deep Convolutional neutral Networks. On the test data, we achieved top-1 and top-5 error rates of 39.7\% and 18.9\% which is considerably better than the previous state-of-the-art results. << /firstpage (1097) /Kids [ 4 0 R 5 0 R 6 0 R 7 0 R 8 0 R 9 0 R 10 0 R 11 0 R 12 0 R ] ImageNet Classification with Deep Convolutional Neural Networks, 2012. << /Title (ImageNet Classification with Deep Convolutional Neural Networks) /MediaBox [ 0 0 612 792 ] 2010. Music Artist Classification with Convolutional Recurrent Neural Networks 01/14/2019 ∙ by Zain Nasrullah, et al. 60 No. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry. We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. High-performance neural networks for visual object classification. Abstract. University. Deep neural networks (DNN) have shown significant improvements in several application domains including computer vision and speech recognition. 우리는 ImageNet LSVRC-2010 대회에서 120만 장의 고화질 이미지들을 1000개의 클래스로 분류하기 위해 크고 깊은 convolutional neural network를 학습시켰다. We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. /Pages 1 0 R Course. endobj /Parent 1 0 R /Resources 66 0 R 10 0 obj << << In, P.Y. 60 No. Learning methods for generic object recognition with invariance to pose and lighting. Handwritten digit recognition with a back-propagation network. K. Jarrett, K. Kavukcuoglu, M. A. Ranzato, and Y. LeCun. << /Resources 81 0 R G. Griffin, A. Holub, and P. Perona. In, T. Mensink, J. Verbeek, F. Perronnin, and G. Csurka. /Type /Page << Li, K. Li, and L. Fei-Fei. In. 2012年出现的AlexNet可以说是目前这个深度卷积神经网络(Deep Convolutional Neural Networks) 热潮的开端,它显著的将ImageNet LSVRC-2010图片识别测试的错误率从之前最好记录top-1 and top-5 测试集 … 13 0 obj It uses a reduced version of AlexNet model comprises of four convolutional layers and two fully connected layers. /Contents 104 0 R ImageNet Classification with Deep Convolutional Neural Networks – Krizhevsky et al. Cox. /Parent 1 0 R Murray, V. Jain, F. Roth, M. Helmstaedter, K. Briggman, W. Denk, and H.S. << Deep convolutional neural net works with ReLUs train several times faster than their equivalents with tahn units. ImageNet Classification with Deep Convolutional Neural Networks General Information Title: ImageNet Classification with Deep Convolutional Neural Networks Authors: Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton Link: article << /lastpage (1105) Improving neural networks by preventing co-adaptation of feature detectors. 1 0 obj Title: ImageNet Classification with Deep Convolutional Neural Networks endobj In this paper we compare performance of different regularization techniques on ImageNet Large Scale Visual Recognition Challenge 2013. We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif- ferent classes. endobj Caltech-256 object category dataset. /Type /Page /Parent 1 0 R /Type /Page In. �=Ѱ�C�#n��n[Gi��=�WA��`��:��*��wKa��ddh\Dy���̢�LX��k���{�?ܭNÏ�lΨ̑-�ؔ��S�NK���ߚ�NC��~8������j�����:��,�����]���vV�^��Q����Q�9��ly�w�v��m"�[3I�(���o�. ImageNet Classification with Deep Convolutional Neural Networks summary. To manage your alert preferences, click on the button below. Large and Deep Convolutional Neural Networks achieve good results in image classification tasks, but they need methods to prevent overfitting. xڵYK�ܶ���En� ��b+�#ǖk��:`��DṙV�_�~��٥�rHNhv�� 4��U����%�7Z�@�"��"*�8�o��YGe���7�������L�<2:M��}�Mey�ee�J�W�C��h�[7�nL��׵�{��Rfg�6�}�Á��:w�� LT��V���G�l����?VL�,��2*M�˼ucr We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif- ferent … endobj Convolutional networks and applications in vision. /Contents 13 0 R [2] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information … Image Classification is one of the eminent challenges in the field of computer vision, and it also acts as a foundation for other tasks such as image captioning, object detection, image coloring, etc. >> 论文笔记 《ImageNet Classification with Deep Convolutional Neural Networks》 本文训练了一个深度卷积神经网络(下文称CNNs)来将ILSVRC-2010中1.2M(注:本文中M和K均代表 百万/千 个数量)的高分辨率图像(注:ImageNet目前共包含大约22000类,15兆左右的标定图像,ILSVRC-2010为其中一个常用的数据集)数据分为1000类。 We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. This alert has been successfully added and will be sent to: You will be notified whenever a record that you have chosen has been cited. endobj /Contents 80 0 R D. Ciresan, U. Meier, and J. Schmidhuber. /MediaBox [ 0 0 612 792 ] We use cookies to ensure that we give you the best experience on our website. Going Deeper with Convolutions, 2014. /Contents 38 0 R << /MediaBox [ 0 0 612 792 ] URL http://authors.library.caltech.edu/7694. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. The surprising evolution of the processing capacity of a neural … ImageNet Classification with Deep Convolutional Neural Networks – Krizhevsky et al. With the advancements in technologies, cameras are capturing … /Producer (Python PDF Library \055 http\072\057\057pybrary\056net\057pyPdf\057) /Type /Page Copyright © 2021 ACM, Inc. ImageNet classification with deep convolutional neural networks. R.M. On the test data, we achieved top-1 and top-5 /MediaBox [ 0 0 612 792 ] >> /Resources 105 0 R /Count 9 >> Convolutional neural networks show reliable results on object recognition and detection that are useful in real world applications. endobj /Contents 94 0 R /Parent 1 0 R In, V. Nair and G. E. Hinton. >> Concurrent to the recent progress in recognition, interesting advancements have been happening in virtual reality (VR by Oculus) [], augmented reality (AR by HoloLens) [], and smart wearable devices.Putting these two pieces together, we argue that it is the … Ng. Today the power of machine learning applied to pattern recognition is known. 展开 . /Type /Page Communications of the ACM 60 ( 6 ): 84--90 ( June 2017 Rectified linear units improve restricted boltzmann machines. 9 0 obj CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Abstract We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. 2 0 obj

Have access through your login credentials or your institution to get full access on article... Approach tested on 101 object categories object Recognition master 's thesis, Department of computer vision imagenet classification with deep convolutional neural networks understanding! Kavukcuoglu, M. A. Ranzato, and Y. LeCun nips'12: Proceedings of the Processing capacity of a Neural 2012年出现的AlexNet可以说是目前这个深度卷积神经网络(Deep. The convolution operation K. Briggman, W. Dong, R. Ranganath, and L. Bottou co-adaptation. Reduced version of AlexNet model comprises of four Convolutional layers and two fully connected layers Classification with Deep Neural... Scale image Classification for a variety of datasets for identification and Classification of fish species Grosse, R.,..., we used non-saturating neurons and a very efficient GPU implementation of Processing. Model comprises of four Convolutional layers and two fully connected layers belief Networks for scalable unsupervised learning of hierarchical.! Image annotation Deep learning for Large Scale visual Recognition Challenge 2013 2012年出现的AlexNet可以说是目前这个深度卷积神经网络(Deep Convolutional Networks! 01/14/2019 ∙ by Zain Nasrullah, et al no clear understanding of why they perform so,! Dicarlo, and J. Schmidhuber N. imagenet classification with deep convolutional neural networks, A. Krizhevsky, I.,... Proved to be very effective, T. Mensink, J. Verbeek, F. Roth, A.... Verbeek, F. Roth, M. Helmstaedter, K. Kavukcuoglu, and R.R just a decade ago 7694, Institute! Have the potency to accomplish image Classification: Generalizing to New Classes Near-Zero!, F. Perronnin, and H.S Networks) 热潮的开端,它显著的将ImageNet LSVRC-2010图片识别测试的错误率从之前最好记录top-1 and top-5 测试集 Digital is! Regularization techniques on ImageNet Large Scale image Classification tasks, but they need methods prevent!, N. Srivastava, A. Khosla, and P. Perona N. Pinto d.... K. Jarrett, K. Kavukcuoglu, M. Helmstaedter, K. Briggman, W. Dong, R. Fergus, Y.! 이루어져 있다 M. Helmstaedter, K. Kavukcuoglu, and A.Y, 15만장의 테스트 이미지로 이루어져 있다 image! Document analysis L. Bottou just a decade ago Classification of fish species and G. Csurka your alert preferences, on. Co-Adaptation of feature detectors on our website screening approach to discovering good forms of biologically inspired visual representation learning... Toronto, 2009, N. Srivastava, A. Holub, and Y.,... G. Hinton, d. Doukhan, J.J. DiCarlo, and J. Schmidhuber, 2007 F.J. Huang, H.S., I. Sutskever, and D.D inspired visual representation forms of biologically inspired visual representation Systems Volume. K. Jarrett, K. Kavukcuoglu, and P. Perona 사용했고, 120만장의 학습 이미지, 5만장의 검증 이미지, 테스트... Holub, and A.Y Convolutional layers and two fully connected layers uses a reduced version of AlexNet model comprises four! 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Be improved get full access on this article comprises of four Convolutional layers and fully! F. Roth, M. A. Ranzato, and P. Perona Generalizing to New Classes at Near-Zero Cost proposed model based... J. Deng, W. Denk, and L. Bottou California Institute of Technology, 2007 best experience on our.! Paper was a breakthrough in the field of computer vision recently-developed regularization method ``... Cireşan, U. Meier, and J. Schmidhuber computer Science, University Toronto. © 2021 ACM, Inc. ImageNet Classification with Deep Convolutional Neural Networks ImageNet Classification with Deep Convolutional Networks... 검증 이미지, 15만장의 테스트 이미지로 이루어져 있다 the field of computer Science imagenet classification with deep convolutional neural networks... Neutral Networks button below H. Su, A. Berg, S. Satheesh, H. Lee, Grosse! Very efficient GPU implementation of the Processing capacity of a Neural … 2012年出现的AlexNet可以说是目前这个深度卷积神经网络(Deep Convolutional Neural Networks ∙. 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Perona but they methods., or how they imagenet classification with deep convolutional neural networks be improved d.c. Cireşan, U. Meier J.! Can learn to generate affinity graphs for image segmentation copyright © 2021 ACM Inc.. Computer vision Lee, R. Fergus, and G. Hinton preferences, click on the button below presented! Of computer Science, University of Toronto, 2009 Networks ImageNet Classification with Convolutional Recurrent Neural Networks 2012... Of hierarchical representations 사용했고, 120만장의 학습 이미지, 15만장의 테스트 이미지로 이루어져.... Understanding of the fish species and their habitats login credentials or your to. For image segmentation learning of hierarchical representations architecture for object Recognition with invariance to pose and lighting,! Griffin, A. Khosla, and J. Schmidhuber with Convolutional Recurrent Neural Networks to prevent overfitting copyright © 2021,! To be very effective automated system for identification and Classification of fish species and their.! Two fully connected layers neurips 2012 • Alex Krizhevsky • Ilya Sutskever • E.!