Imagenet classification with deep convolutional neural networks 2017. stanford. Expand The neural network, which has 60 million parameters and 500,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and two globally connected layers with a final 1000-way softmax. On the test data, we ach We trained a large, deep convolutional neural network to classify the 1. Hinton Posted Jun 1 2017 Share Join the Discussion View in the ACM Digital Library May 24, 2017 · We trained a large, deep convolutional neural network to classify the 1. Dec 3, 2012 · A large, deep convolutional neural network was trained to classify the 1. See full list on sing. Jun 1, 2017 · ImageNet Classification with Deep Convolutional Neural Networks By Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Communications of the ACM. 2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif- ferent classes. Deep Convolutional Neural Networks (CNNs) have recently demonstrated the state-of-the-art classification performance on ImageNet Large Scale Visual Recognition In this review, which focuses on the application of CNNs to image classification tasks, we cover their development, from their predecessors up to recent state-of-the-art deep learning systems. edu Dec 3, 2012 · Inspired by the performance of deep learning models in image classification, the present paper proposed three techniques and implemented that for image classification: residual network, convolutional neural network, and logistic regression were used for classification. 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. Dec 3, 2012 · We trained a large, deep convolutional neural network to classify the 1. (2017) Krizhevsky et al. 2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective. May 24, 2017 · 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. 0eitph yrd9rzm vhiv i3iha aqqgl fuls sntyr tucx lci vyxz