Most cited papers in Domain Adaptation and Few-Shot Learning
What are the most cited papers in Domain Adaptation and Few-Shot Learning?
The most cited paper in Domain Adaptation and Few-Shot Learning is "Deep Residual Learning for Image Recognition" (Kaiming He, Xiangyu Zhang, Shaoqing Ren et al., 2016), with 229,585 citations. Next come "Long Short-Term Memory" (102,418) and "ImageNet classification with deep convolutional neural networks" (75,634).
Showing 50 of 97 papers. Every paper with more than 2,928 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | Deep Residual Learning for Image Recognition Kaiming He, Xiangyu Zhang, Shaoqing Ren et al. | Jun 2016 | 229,585 |
| 2 | Long Short-Term Memory Sepp Hochreiter, Jürgen Schmidhuber · Neural Computation | Nov 1997 | 102,418 |
| 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 | Going deeper with convolutions Christian Szegedy, Wei Liu, Yangqing Jia et al. | Jun 2015 | 47,422 |
| 5 | Densely Connected Convolutional Networks Gao Ming Huang, Zhuang Liu, Laurens van der Maaten et al. | Jul 2017 | 45,976 |
| 6 | Fully convolutional networks for semantic segmentation Jonathan Long, Evan Shelhamer, Trevor J. Darrell | Jun 2015 | 37,703 |
| 7 | 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 |
| 8 | Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation Ross Girshick, Jeff Donahue, Trevor J. Darrell et al. | Jun 2014 | 32,284 |
| 9 | Squeeze-and-Excitation Networks Jie Hu, Li Shen, Gang Zheng Sun | Jun 2018 | 30,332 |
| 10 | Feature Pyramid Networks for Object Detection Tsung-Yi Lin, Piotr Dollár, Ross Girshick et al. | Jul 2017 | 29,985 |
| 11 | Focal Loss for Dense Object Detection Tsung-Yi Lin, Priya Goyal, Ross Girshick et al. | Oct 2017 | 27,432 |
| 12 | Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Sergey Ioffe, Christian Szegedy · arXiv (Cornell University) | 11 Feb 2015 | 24,392 |
| 13 | A Survey on Transfer Learning Sinno Jialin Pan, Qiang Yang · IEEE Transactions on Knowledge and Data Engineering | 16 Oct 2009 | 23,834 |
| 14 | CBAM: Convolutional Block Attention Module Sanghyun Woo, Jongchan Park, Joon‐Young Lee et al. · Lecture notes in computer science | 2018 | 23,707 |
| 15 | Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Kumar Das et al. | Oct 2017 | 23,064 |
| 16 | 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 |
| 17 | The Pascal Visual Object Classes (VOC) Challenge Mark Everingham, Luc Van Gool, Christopher K. I. Williams et al. · International Journal of Computer Vision | 8 Sep 2009 | 20,137 |
| 18 | Xception: Deep Learning with Depthwise Separable Convolutions François Chollet | Jul 2017 | 19,596 |
| 19 | Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification Kaiming He, Xiangyu Zhang, Shaoqing Ren et al. | Dec 2015 | 19,129 |
| 20 | Deep learning in neural networks: An overview Jürgen Schmidhuber · Neural Networks | 13 Oct 2014 | 18,327 |
| 21 | A Fast Learning Algorithm for Deep Belief Nets Geoffrey E. Hinton, Simon Osindero, Yee‐Whye Teh · Neural Computation | 29 May 2006 | 16,607 |
| 22 | Detecting Functionality-Specific Vulnerabilities via Retrieving Individual Functionality-Equivalent APIs in Open-Source Repositories Chen, Tianyu, Wang, Zeyu, Li, Lin et al. · Leibniz international proceedings in informatics | 2025 | 16,321 |
| 23 | Representation Learning: A Review and New Perspectives Yoshua Bengio, Aaron C. Courville, P. M. Durai Raj Vincent · IEEE Transactions on Pattern Analysis and Machine Intelligence | 31 May 2013 | 13,351 |
| 24 | Rectified Linear Units Improve Restricted Boltzmann Machines Vinod B Nair, Geoffrey E. Hinton · International Conference on Machine Learning | 21 Jun 2010 | 13,220 |
| 25 | Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke et al. · AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) | 12 Feb 2017 | 12,801 |
| 26 | Momentum Contrast for Unsupervised Visual Representation Learning Kaiming He, Haoqi Fan, Yuxin Wu et al. | Jun 2020 | 12,628 |
| 27 | Aggregated Residual Transformations for Deep Neural Networks Saining Xie, Ross Girshick, Piotr Dollár et al. | Jul 2017 | 12,067 |
| 28 | 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 |
| 29 | 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 |
| 30 | Robust Face Recognition via Sparse Representation John N. Wright, A. Yang, Arvind Ganesh et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence | 14 Jan 2009 | 9,546 |
| 31 | EfficientDet: Scalable and Efficient Object Detection Mingxing Tan, Ruoming Pang, Quoc Viet Le | Jun 2020 | 9,034 |
| 32 | ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks Qilong Wang, Banggu Wu, Pengfei Zhu et al. | Jun 2020 | 8,753 |
| 33 | Learning long-term dependencies with gradient descent is difficult Yoshua Bengio, P. Simard, Paolo Frasconi · IEEE Transactions on Neural Networks | Mar 1994 | 8,685 |
| 34 | A ConvNet for the 2020s Zhuang Liu, Hanzi Mao, Chao-Yuan Wu et al. · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) | Jun 2022 | 8,057 |
| 35 | Semi-Supervised Classification with Graph Convolutional Networks Thomas Kipf, Max Welling · arXiv (Cornell University) | 9 Sep 2016 | 8,050 |
| 36 | Overcoming catastrophic forgetting in neural networks James Kirkpatrick, Razvan Pascanu, Neil C. Rabinowitz et al. · Proceedings of the National Academy of Sciences | 14 Mar 2017 | 7,777 |
| 37 | Domain-Adversarial Training of Neural Networks Yaroslav Ganin, Evgeniya Ustinova, Hana Ajakan et al. · Advances in computer vision and pattern recognition | 2017 | 7,721 |
| 38 | The Pascal Visual Object Classes Challenge: A Retrospective Mark Everingham, S. M. Ali Eslami, Luc Van Gool et al. · International Journal of Computer Vision | 24 Jun 2014 | 7,447 |
| 39 | A Simple Framework for Contrastive Learning of Visual Representations Ting Chen, Simon Kornblith, Mohammad Norouzi et al. · arXiv (Cornell University) | 13 Feb 2020 | 7,327 |
| 40 | Cascade R-CNN: Delving Into High Quality Object Detection Zhaowei Cai, Nuno M. Vasconcelos | Jun 2018 | 7,067 |
| 41 | Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks Alec Radford, Luke Metz, Soumith Chintala · arXiv (Cornell University) | 19 Nov 2015 | 6,972 |
| 42 | Improving neural networks by preventing co-adaptation of feature detectors Geoffrey E. Hinton, Nitish Srivastava, Alex Krizhevsky et al. · arXiv (Cornell University) | 3 Jul 2012 | 6,640 |
| 43 | Multitask Learning Rich Caruana · Machine Learning | Jul 1997 | 6,547 |
| 44 | A Comprehensive Survey on Transfer Learning Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan et al. · Proceedings of the IEEE | 7 Jul 2020 | 6,487 |
| 45 | A survey of transfer learning Karl R. Weiss, Taghi M. Khoshgoftaar, Dingding Wang · Journal Of Big Data | 27 May 2016 | 6,327 |
| 46 | Coordinate Attention for Efficient Mobile Network Design Qibin Hou, Daquan Zhou, Jiashi Feng | Jun 2021 | 6,257 |
| 47 | Wide Residual Networks Sergey Zagoruyko, Nikos Komodakis | 2016 | 6,132 |
| 48 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Chelsea Finn, Pieter Abbeel, Sergey Levine · arXiv (Cornell University) | 9 Mar 2017 | 5,777 |
| 49 | Learning to Forget: Continual Prediction with LSTM Felix A. Gers, Jürgen Schmidhuber, Fred A. Cummins · Neural Computation | Oct 2000 | 5,583 |
| 50 | Emerging Properties in Self-Supervised Vision Transformers Mathilde Caron, Hugo Touvron, Ishan Misra et al. · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) | Oct 2021 | 5,566 |
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.