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Papers on “convolutional neural networks image recognition classification”

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1. ImageNet classification with deep convolutional neural networks

   Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton · 2017 · Communications of the ACM · 75,617 cites

   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. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, which is considerably better than the previous state-of-the-art. 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. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitt

2. Very Deep Convolutional Networks for Large-Scale Image Recognition

   Karen Simonyan, Andrew Zisserman · 2014 · arXiv (Cornell University) · 75,434 cites

   In this work we investigate the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting. Our main contribution is a thorough evaluation of networks of increasing depth using an architecture with very small (3x3) convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. 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. We also show that our representations generalise well to other datasets, where they achieve stat

3. Gradient-based learning applied to document recognition

   Yann LeCun, Léon Bottou, Yoshua Bengio, et al. · 1998 · Proceedings of the IEEE · 59,642 cites

   Multilayer neural networks trained with the back-propagation algorithm constitute the best example of a successful gradient based learning technique. Given an appropriate network architecture, gradient-based learning algorithms can be used to synthesize a complex decision surface that can classify high-dimensional patterns, such as handwritten characters, with minimal preprocessing. This paper reviews various methods applied to handwritten character recognition and compares them on a standard handwritten digit recognition task. Convolutional neural networks, which are specifically designed to deal with the variability of 2D shapes, are shown to outperform all other techniques. Real-life docu

4. Support-vector networks

   Corinna Cortes, Vladimir Vapnik · 1995 · Machine Learning · 41,126 cites

5. Convolutional Neural Networks for Sentence Classification

   Yoon Kim · 2014 · 14,018 cites

   We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks.We show that a simple CNN with little hyperparameter tuning and static vectors achieves excellent results on multiple benchmarks.Learning task-specific vectors through fine-tuning offers further gains in performance.We additionally propose a simple modification to the architecture to allow for the use of both task-specific and static vectors.The CNN models discussed herein improve upon the state of the art on 4 out of 7 tasks, which include sentiment analysis and question classification.

6. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

   Kaiming He, Xiangyu Zhang, Shaoqing Ren, et al. · 2015 · IEEE Transactions on Pattern Analysis and Machine Intelligence · 11,567 cites

   Existing deep convolutional neural networks (CNNs) require a fixed-size (e.g., 224 × 224) input image. This requirement is "artificial" and may reduce the recognition accuracy for the images or sub-images of an arbitrary size/scale. In this work, we equip the networks with another pooling strategy, "spatial pyramid pooling", to eliminate the above requirement. The new network structure, called SPP-net, can generate a fixed-length representation regardless of image size/scale. Pyramid pooling is also robust to object deformations. With these advantages, SPP-net should in general improve all CNN-based image classification methods. On the ImageNet 2012 dataset, we demonstrate that SPP-net boost

7. A Comprehensive Survey on Graph Neural Networks

   Zonghan Wu, Shirui Pan, Fengwen Chen, et al. · 2020 · IEEE Transactions on Neural Networks and Learning Systems · 9,947 cites

   Deep learning has revolutionized many machine learning tasks in recent years, ranging from image classification and video processing to speech recognition and natural language understanding. The data in these tasks are typically represented in the Euclidean space. However, there is an increasing number of applications, where data are generated from non-Euclidean domains and are represented as graphs with complex relationships and interdependency between objects. The complexity of graph data has imposed significant challenges on the existing machine learning algorithms. Recently, many studies on extending deep learning approaches for graph data have emerged. In this article, we provide a comp

8. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

   Andrew Howard, Menglong Zhu, Bo Chen, et al. · 2017 · arXiv (Cornell University) · 9,876 cites

   We present a class of efficient models called MobileNets for mobile and embedded vision applications. MobileNets are based on a streamlined architecture that uses depth-wise separable convolutions to build light weight deep neural networks. We introduce two simple global hyper-parameters that efficiently trade off between latency and accuracy. These hyper-parameters allow the model builder to choose the right sized model for their application based on the constraints of the problem. We present extensive experiments on resource and accuracy tradeoffs and show strong performance compared to other popular models on ImageNet classification. We then demonstrate the effectiveness of MobileNets acr