Deeper neural networks are more difficult to train. Kaiming He, et al., "Deep Residual Learning for Image Recognition" https://arxiv.org/abs/1512.03385 2015年のILSVRCで1位になったディープラーニングモデルであるResNetの論文の"Deep Residual L…

We have made our two best-performing ConvNet models publicly available to facilitate further research on the use of deep visual representations in computer vision. Karen Simonyan, et al., "Very Deep Convolutional Networks for Large-Scale I…

We also show that our representations generalise well to other datasets, where they achieve state-of-the-art results. Karen Simonyan, et al., "Very Deep Convolutional Networks for Large-Scale Image Recognition" https://arxiv.org/abs/1409.1…

These findings were the basis of our ImageNet Challenge 2014 submission, where our team secured the first and the second places in the localisation and classification tracks respectively. Karen Simonyan, et al., "Very Deep Convolutional Ne…

Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3 × 3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by…

In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Karen Simonyan, et al., "Very Deep Convolutional Networks for Large-Scale Image Recognition" https://ar…

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. Alex Krizhevsky, et al., "ImageNet Classification with Deep …

To reduce overfitting in the fully-connected layers we employed a recently-developed regularization method called “dropout” that proved to be very effective. Alex Krizhevsky, et al., "ImageNet Classification with Deep Convolutional Neural …

To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. Alex Krizhevsky, et al., "ImageNet Classification with Deep Convolutional Neural Networks" https://papers.nips.cc…

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. Alex Krizhevs…

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. Alex Krizhevsky, et al., "ImageNet Classification with Deep Convolutional Neural Networks" https:…

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. Alex Krizhevsky, et al., "ImageNet Classification with Deep Convol…