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Research papers on Neural networks for image recognition

Recent and highly-cited academic work on neural networks for image recognition, gathered from Semantic Scholar, CrossRef and OpenAlex.

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  1. ImageNet classification with deep convolutional neural networks

    Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton · 2017 · Communications of the ACM · 75,666 citations

    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

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

    Karen Simonyan, Andrew Zisserman · 2014 · arXiv (Cornell University) · 75,490 citations

    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

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  3. Gradient-based learning applied to document recognition

    Yann LeCun, Léon Bottou, Yoshua Bengio, et al. · 1998 · Proceedings of the IEEE · 59,475 citations

    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

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  4. Convolutional Neural Networks for Sentence Classification

    Yoon Kim · 2014 · 13,987 citations

    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.

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  5. 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 · 11,539 citations

    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

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  6. Non-local Neural Networks

    Xiaolong Wang, Ross Girshick, Abhinav Gupta, et al. · 2018 · 11,413 citations

    Both convolutional and recurrent operations are building blocks that process one local neighborhood at a time. In this paper, we present non-local operations as a generic family of building blocks for capturing long-range dependencies. Inspired by the classical non-local means method in computer vision, our non-local operation computes the response at a position as a weighted sum of the features at all positions. This building block can be plugged into many computer vision architectures. On the task of video classification, even without any bells and whistles, our non-local models can compete or outperform current competition winners on both Kinetics and Charades datasets. In static image re

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

    Andrew Howard, Menglong Zhu, Bo Chen, et al. · 2017 · arXiv (Cornell University) · 9,885 citations

    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

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  8. A Comprehensive Survey on Graph Neural Networks

    Zonghan Wu, Shirui Pan, Fengwen Chen, et al. · 2020 · IEEE Transactions on Neural Networks and Learning Systems · 9,855 citations

    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

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  9. Large-Scale Video Classification with Convolutional Neural Networks

    Andrej Karpathy, George Toderici, Sanketh Shetty, et al. · 2014 · 6,380 citations

    Convolutional Neural Networks (CNNs) have been established as a powerful class of models for image recognition problems. Encouraged by these results, we provide an extensive empirical evaluation of CNNs on large-scale video classification using a new dataset of 1 million YouTube videos belonging to 487 classes. We study multiple approaches for extending the connectivity of a CNN in time domain to take advantage of local spatio-temporal information and suggest a multiresolution, foveated architecture as a promising way of speeding up the training. Our best spatio-temporal networks display significant performance improvements compared to strong feature-based baselines (55.3% to 63.9%), but onl

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  10. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning

    Hoo-Chang Shin, Holger R. Roth, Mingchen Gao, et al. · 2016 · IEEE Transactions on Medical Imaging · 5,874 citations

    Remarkable progress has been made in image recognition, primarily due to the availability of large-scale annotated datasets and deep convolutional neural networks (CNNs). CNNs enable learning data-driven, highly representative, hierarchical image features from sufficient training data. However, obtaining datasets as comprehensively annotated as ImageNet in the medical imaging domain remains a challenge. There are currently three major techniques that successfully employ CNNs to medical image classification: training the CNN from scratch, using off-the-shelf pre-trained CNN features, and conducting unsupervised CNN pre-training with supervised fine-tuning. Another effective method is transfer

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  11. Understanding of a convolutional neural network

    Saad Albawi, Tareq Abed Mohammed, Saad Al-Azawi · 2017 · 4,024 citations

    The term Deep Learning or Deep Neural Network refers to Artificial Neural Networks (ANN) with multi layers. Over the last few decades, it has been considered to be one of the most powerful tools, and has become very popular in the literature as it is able to handle a huge amount of data. The interest in having deeper hidden layers has recently begun to surpass classical methods performance in different fields; especially in pattern recognition. One of the most popular deep neural networks is the Convolutional Neural Network (CNN). It take this name from mathematical linear operation between matrixes called convolution. CNN have multiple layers; including convolutional layer, non-linearity la

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  12. Multi-column deep neural networks for image classification

    Dan Cireşan, Ueli Meier, Jürgen Schmidhuber · 2012 · 3,776 citations

    Traditional methods of computer vision and machine learning cannot match human performance on tasks such as the recognition of handwritten digits or traffic signs. Our biologically plausible, wide and deep artificial neural network architectures can. Small (often minimal) receptive fields of convolutional winner-take-all neurons yield large network depth, resulting in roughly as many sparsely connected neural layers as found in mammals between retina and visual cortex. Only winner neurons are trained. Several deep neural columns become experts on inputs preprocessed in different ways; their predictions are averaged. Graphics cards allow for fast training. On the very competitive MNIST handwr

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  18. Modified Convolutional Neural Networks Architecture for Hyperspectral Image Classification (Extra‐Convolutional Neural Networks)

    Maissa HAMOUDA, Med Salim BOUHLEL · 2025 · IET Image Processing · 7 citations

    Abstract Classification of Hyperspectral Satellite Images (HSI) is a very important technology for object detection and cartography. Several problems can be detected, which make classification difficult (large size of the images, fusion between the classes, small amount of samples, etc.). Recently, several Convolutional Neural Networks (CNN‐HSI) have been proposed for the classification of hyperspectral images. In this article, an improvement to CNN‐HSI is proposed, aiming to reduce the number of erroneous pixels during classification (due to the limited number of samples). Thus, an extra‐convolution technique (ExCNN) is proposed, where we add layers of global convolutions

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  19. Creating Deep Convolutional Neural Networks for Image Classification

    Nabeel Siddiqui · 2023 · Programming Historian · 2 citations

    This lesson provides a beginner-friendly introduction to convolutional neural networks (CNNs) for image classification. The tutorial provides a conceptual understanding of how neural networks work by using Google's Teachable Machine to train a model on paintings from the ArtUK database. This lesson also demonstrates how to use Javascript to embed the model in a live website.

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  20. Application of convolutional neural networks in image classification and applications of improved convolutional neural networks

    Taoyu Liu · 2024 · Applied and Computational Engineering · 2 citations

    This paper reviews the application and improvement of convolutional neural networks (CNNs) in image classification. Firstly, a shallow CNN for interstitial lung disease image classification is presented. This model suppresses overfitting through a unique network architecture and optimisation algorithm. Next, the improved VGG16 architecture and MIDNet18 model are discussed and their superior performance in brain tumour image classification is demonstrated. Subsequently, a CNN-CapsNet model for cervical cancer image classification and its improvement are presented and the customised model is compared with the conventional VGG-16 CNN architecture in the paper. Next, the application of sparse co

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