Most cited papers in Advanced Neural Network Applications
What are the most cited papers in Advanced Neural Network Applications?
The most cited paper in Advanced Neural Network Applications is "Deep Residual Learning for Image Recognition" (Kaiming He, Xiangyu Zhang, Shaoqing Ren et al., 2016), with 229,585 citations. Next come "U-Net: Convolutional Networks for Biomedical Image Segmentation" (92,751) and "ImageNet classification with deep convolutional neural networks" (75,634).
Showing 50 of 95 papers. Every paper with more than 4,077 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Deep Residual Learning for Image Recognition Kaiming He, Xiangyu Zhang, Shaoqing Ren et al. | Jun 2016 | 229,585 |
| 2 | U-Net: Convolutional Networks for Biomedical Image Segmentation Olaf Ronneberger, Philipp F. Fischer, Thomas Brox · Lecture notes in computer science | 2015 | 92,751 |
| 3 | ImageNet classification with deep convolutional neural networks Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton · Communications of the ACM | 24 May 2017 | 75,634 |
| 4 | Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan, Andrew Zisserman · arXiv (Cornell University) | 4 Sep 2014 | 75,434 |
| 5 | Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks Shaoqing Ren, Kaiming He, Ross Girshick et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence | 6 Jun 2016 | 55,999 |
| 6 | Going deeper with convolutions Christian Szegedy, Wei Liu, Yangqing Jia et al. | Jun 2015 | 47,422 |
| 7 | Densely Connected Convolutional Networks Gao Ming Huang, Zhuang Liu, Laurens van der Maaten et al. | Jul 2017 | 45,976 |
| 8 | Microsoft COCO: Common Objects in Context Tsung-Yi Lin, Michael Maire, Serge Belongie et al. · Lecture notes in computer science | 2014 | 43,802 |
| 9 | ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky, Jia Hao Deng, Hao Su et al. · International Journal of Computer Vision | 10 Apr 2015 | 40,843 |
| 10 | Fully convolutional networks for semantic segmentation Jonathan Long, Evan Shelhamer, Trevor J. Darrell | Jun 2015 | 37,703 |
| 11 | Swin Transformer: Hierarchical Vision Transformer using Shifted Windows Ze Liu, Yutong Lin, Yue Cao et al. · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) | Oct 2021 | 32,838 |
| 12 | Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation Ross Girshick, Jeff Donahue, Trevor J. Darrell et al. | Jun 2014 | 32,284 |
| 13 | Rethinking the Inception Architecture for Computer Vision Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe et al. | Jun 2016 | 31,624 |
| 14 | SSD: Single Shot MultiBox Detector Wei Liu, Dragomir Anguelov, Dumitru Erhan et al. | 16 Jan 2016 | 30,509 |
| 15 | Squeeze-and-Excitation Networks Jie Hu, Li Shen, Gang Zheng Sun | Jun 2018 | 30,332 |
| 16 | Feature Pyramid Networks for Object Detection Tsung-Yi Lin, Piotr Dollár, Ross Girshick et al. | Jul 2017 | 29,985 |
| 17 | Mask R-CNN Kaiming He, Georgia Gkioxari, Piotr Dollár et al. | Oct 2017 | 29,752 |
| 18 | Fast R-CNN Ross Girshick | Dec 2015 | 28,569 |
| 19 | Focal Loss for Dense Object Detection Tsung-Yi Lin, Priya Goyal, Ross Girshick et al. | Oct 2017 | 27,432 |
| 20 | MobileNetV2: Inverted Residuals and Linear Bottlenecks Mark Sandler, Andrew Howard, Menglong Zhu et al. | Jun 2018 | 26,808 |
| 21 | CBAM: Convolutional Block Attention Module Sanghyun Woo, Jongchan Park, Joon‐Young Lee et al. · Lecture notes in computer science | 2018 | 23,707 |
| 22 | DeepLab: Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs Liang-Chieh Chen, George Papandreou, Iasonas Kokkinos et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence | 27 Apr 2017 | 22,754 |
| 23 | An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov et al. · arXiv (Cornell University) | 22 Oct 2020 | 22,044 |
| 24 | Xception: Deep Learning with Depthwise Separable Convolutions François Chollet | Jul 2017 | 19,596 |
| 25 | YOLO9000: Better, Faster, Stronger Joseph Redmon, Ali Farhadi | Jul 2017 | 19,320 |
| 26 | Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification Kaiming He, Xiangyu Zhang, Shaoqing Ren et al. | Dec 2015 | 19,129 |
| 27 | Pyramid Scene Parsing Network Hengshuang Zhao, Jianping Shi, Xiaojuan Qi et al. | Jul 2017 | 15,943 |
| 28 | Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Sergey Ioffe · arXiv (Cornell University) | 2024 | 15,638 |
| 29 | Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation Liang-Chieh Chen, Yukun Zhu, George Papandreou et al. · Lecture notes in computer science | 2018 | 15,130 |
| 30 | Are we ready for autonomous driving? The KITTI vision benchmark suite Andreas Geiger, Pascal Lenz, R. Urtasun | Jun 2012 | 14,924 |
| 31 | SURF: Speeded Up Robust Features Herbert Bay, Tinne Tuytelaars, Luc Van Gool · Lecture notes in computer science | 2006 | 14,761 |
| 32 | A survey on Image Data Augmentation for Deep Learning Connor Shorten, Taghi M. Khoshgoftaar · Journal Of Big Data | 6 Jul 2019 | 13,152 |
| 33 | Momentum Contrast for Unsupervised Visual Representation Learning Kaiming He, Haoqi Fan, Yuxin Wu et al. | Jun 2020 | 12,628 |
| 34 | The Cityscapes Dataset for Semantic Urban Scene Understanding Marius Cordts, Mohamed Omran, Sebastian Ramos et al. | Jun 2016 | 12,301 |
| 35 | Aggregated Residual Transformations for Deep Neural Networks Saining Xie, Ross Girshick, Piotr Dollár et al. | Jul 2017 | 12,067 |
| 36 | YOLOv7: Trainable Bag-of-Freebies Sets New State-of-the-Art for Real-Time Object Detectors Chien-Yao Wang, Alexey Bochkovskiy, Hong-Yuan Mark Liao | Jun 2023 | 11,754 |
| 37 | Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition Kaiming He, Xiangyu Zhang, Shaoqing Ren et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence | 9 Jan 2015 | 11,594 |
| 38 | Non-local Neural Networks Xiaolong Wang, Ross Girshick, Abhinav Gupta et al. | Jun 2018 | 11,465 |
| 39 | Fully Convolutional Networks for Semantic Segmentation Evan Shelhamer, Jonathan Long, Trevor J. Darrell · IEEE Transactions on Pattern Analysis and Machine Intelligence | 24 May 2016 | 11,276 |
| 40 | Caffe Yangqing Jia, Evan Shelhamer, Jeff Donahue et al. | 3 Nov 2014 | 11,245 |
| 41 | Fast robust automated brain extraction Stephen M. Smith · Human Brain Mapping | 18 Sep 2002 | 10,948 |
| 42 | YOLOv4: Optimal Speed and Accuracy of Object Detection Alexey Bochkovskiy, Chien-Yao Wang, Hong-Yuan Mark Liao · arXiv (Cornell University) | 23 Apr 2020 | 10,541 |
| 43 | Identity Mappings in Deep Residual Networks Kaiming He, Xiangyu Zhang, Shaoqing Ren et al. · Lecture notes in computer science | 2016 | 10,296 |
| 44 | Object Detection with Discriminatively Trained Part-Based Models Pedro F. Felzenszwalb, Ross Girshick, David McAllester et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence | 24 Sep 2009 | 10,129 |
| 45 | Vision meets robotics: The KITTI dataset Andreas Geiger, Philip Lenz, Christoph Stiller et al. · The International Journal of Robotics Research | 23 Aug 2013 | 10,105 |
| 46 | nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation Fabian Isensee, Paul F. Jaeger, Simon A. A. Kohl et al. · Nature Methods | 7 Dec 2020 | 9,588 |
| 47 | UNet++: A Nested U-Net Architecture for Medical Image Segmentation Zongwei Zhou, Md Mahfuzur Rahman Siddiquee, Nima Tajbakhsh et al. · Lecture notes in computer science | 2018 | 9,545 |
| 48 | LightGBM: A Highly Efficient Gradient Boosting Decision Tree Guolin Ke, Qi Meng, Thomas Finley et al. · HAL (Le Centre pour la Communication Scientifique Directe) | 4 Dec 2017 | 9,485 |
| 49 | ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices Xiangyu Zhang, Xinyu Zhou, Mengxiao Lin et al. | Jun 2018 | 9,334 |
| 50 | Path Aggregation Network for Instance Segmentation Shu Liu, Lu Qi, Haifang Qin et al. | Jun 2018 | 9,270 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.