The DOI matches its Crossref record; 274 of the 286 references it cites were found in a registry or an index.
Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review
Abstract
Convolutional neural networks (CNNs) have been applied to visual tasks since the late 1980s. However, despite a few scattered applications, they were dormant until the mid-2000s when developments in computing power and the advent of large amounts of labeled data, supplemented by improved algorithms, contributed to their advancement and brought them to the forefront of a neural network renaissance that has seen rapid progression since 2012. In this review, which focuses on the application of CNNs to image classification tasks, we cover their development, from their predecessors up to recent state-of-the-art deep learning systems. Along the way, we analyze (1) their early successes, (2) their role in the deep learning renaissance, (3) selected symbolic works that have contributed to their recent popularity, and (4) several improvement attempts by reviewing contributions and challenges of over 300 publications. We also introduce some of their current trends and remaining challenges.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Therefore, the output features include the specific pose information of objects, which means that CapsNet has the ability to recognize the orientation.”
From A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects (2022) · cites this paper as [186]
In the citing paper
“Some experiments have proved that CapsNet is able to reach state-ofthe-art results.”
From A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects (2022) · cites this paper as [186]
In the citing paper
“Although it did not achieve satisfactory results on complicated large-scale data sets, like CIFAR-100 or ImageNet, we can see that it is potential.”
From A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects (2022) · cites this paper as [186]
Citations
Cites
Items 201–274 of 274
Learning long‐range vision for autonomous off‐road driving
Hadsell, R. T., Sermanet, P., Ben, J., Erkan, A., Scoffier, M., Kavukcuoglu, K., Müller, U., & LeCun, Y. (2009) · Journal of Field Robotics · doi:10.1002/rob.20276 · Cited by 314
Deep Generative Stochastic Networks Trainable by BackpropPreprint
Bengio, Y., Thibodeau-Laufer, É., Alain, G., & Yosinski, J. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1306.1091 · Cited by 305
Applications of advances in nonlinear sensitivity analysis
Werbos, P. J. (2005) · Springer eBooks · doi:10.1007/bfb0006203 · Cited by 289
Learning invariant features through topographic filter maps
Kavukcuoglu, K., Ranzato, M., Fergus, R., & LeCun, Y. (2009) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2009.5206545 · Cited by 278
High-dimensional signature compression for large-scale image classification
Sanchez, J., & Perronnin, F. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995504 · Cited by 272
Toward automatic phenotyping of developing embryos from videos
Ning, F., Delhomme, D., LeCun, Y., Piano, F., Bottou, L., & Barbano, P. E. (2005) · IEEE Transactions on Image Processing · doi:10.1109/tip.2005.852470 · Cited by 267
TI-POOLING: Transformation-Invariant Pooling for Feature Learning in Convolutional Neural Networks
Laptev, D., Savinov, N., Buhmann, J. M., & Pollefeys, M. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.38 · Cited by 265
Using GPUs for machine learning algorithms
Steinkraus, D., Buck, I., & Simard, P. Y. (2005) · Eighth International Conference on Document Analysis and Recognition (ICDAR'05) · doi:10.1109/icdar.2005.251 · Cited by 251
Provable Bounds for Learning Some Deep RepresentationsPreprint
Arora, S., Bhaskara, A., Ge, R., & Ma, T. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1310.6343 · Cited by 251
Improvements to Deep Convolutional Neural Networks for LVCSR
Sainath, T. N., Kingsbury, B. E. D., Mohamed, A., Dahl, G. E., Saon, G., Soltau, H., Beran, T., Aravkin, A. Y., & Ramabhadran, B. (2013) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2013.6707749 · Cited by 247
A Taxonomy of Deep Convolutional Neural Nets for Computer Vision
Srinivas, S., Sarvadevabhatla, R. K., Mopuri, K. R., Prabhu, N., Kruthiventi, S. S. S., & Babu, R. V. (2016) · Frontiers in Robotics and AI · doi:10.3389/frobt.2015.00036 · Cited by 244
Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence
Albrecht, S. V., Crandall, J. W., & Ramamoorthy, R. (2015) · National Conference on Artificial Intelligence · Cited by 243
Smirnov, E. A., Timoshenko, D. M., & Andrianov, S. N. (2014) · AASRI Procedia · doi:10.1016/j.aasri.2014.05.013 · Cited by 237
DisturbLabel: Regularizing CNN on the Loss Layer
Xie, L., Wang, J., Wei, Z., Wang, M., & Tian, Q. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.514 · Cited by 236
Fast Inference in Sparse Coding Algorithms with Applications to Object RecognitionPreprint
Kavukcuoglu, K., Ranzato, M., & LeCun, Y. (2010) · arXiv (Cornell University) · doi:10.48550/arxiv.1010.3467 · Cited by 222
Liu, B., Li, F., Wang, X., Zhang, B., & Yan, J. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10094626 · Cited by 217
Semi-supervised learning of compact document representations with deep networks
Ranzato, M., & Szummer, M. (2008) · International Conference on Machine Learning · doi:10.1145/1390156.1390256 · Cited by 215
Deep Learning with S-Shaped Rectified Linear Activation Units
Jin, X., Xu, C., Feng, J., Wei, Y., Xiong, J., & Yan, S. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10287 · Cited by 214
A Practical Transfer Learning Algorithm for Face Verification
Cao, X., Wipf, D., Wen, F., Duan, G., & Sun, J. Z. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.398 · Cited by 199
Naive-Deep Face Recognition: Touching the Limit of LFW Benchmark or Not?Preprint
Zhou, E., Cao, Z., & Yin, Q. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1501.04690 · Cited by 192
Exploring the space of adversarial images
Tabacof, P., & Junior, E. A. D. V. (2016) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727230 · Cited by 188
Learned-Norm Pooling for Deep Feedforward and Recurrent Neural Networks
Gülçehre, Ç., Cho, K., Pascanu, R., & Bengio, Y. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-44848-9_34 · Cited by 177
Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers
Farabet, C., Couprie, C., Najman, L. A., & LeCun, Y. (2012) · HAL · doi:10.48550/arxiv.1202.2160 · Cited by 176
Max-Pooling Dropout for Regularization of Convolutional Neural Networks
Wu, H., & Gu, X. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-26532-2_6 · Cited by 174
Fast High Dimensional Vector Multiplication Face Recognition
Barkan, O., Weill, J., Wolf, L., & Aronowitz, H. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.246 · Cited by 173
The numerical solution of variational problems
Dreyfus, S. E. (1962) · Journal of Mathematical Analysis and Applications · doi:10.1016/0022-247x(62)90004-5 · Cited by 172
Kim, M., & Smaragdis, P. (2016) · arXiv · doi:10.48550/arxiv.1601.06071 · Cited by 166
8-Bit Approximations for Parallelism in Deep LearningPreprint
Dettmers, T. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.04561 · Cited by 164
Ahmed, A. A., Yu, K., Xu, W. H., Gong, Y., & Xing, E. P. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88690-7_6 · Cited by 153
Ivakhnenko, A. G., & Lapa, V. G. (1966) · CCM Information eBooks · Cited by 150
Understanding Convolutional Neural NetworksPreprint
Koushik, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1605.09081 · Cited by 150
A fast parallel SGD for matrix factorization in shared memory systems
Zhuang, Y., Chin, W.-S., Juan, Y.-C., & Lin, C. (2013) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/2507157.2507164 · Cited by 142
On-Line Learning in Neural Networks
Saad, D. (1999) · Cambridge University Press eBooks · doi:10.1017/cbo9780511569920 · Cited by 140
Neural Decision Forests for Semantic Image Labelling
Bulò, S. R., & Kontschieder, P. (2014) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2014.18 · Cited by 133
VC dimension of neural networks
Sontag, E. D. (1998) · Cited by 120
STDP-Compatible Approximation of Backpropagation in an Energy-Based Model
Bengio, Y., Mesnard, T., Fischer, A., Zhang, S., & Wu, Y. (2017) · Neural Computation · doi:10.1162/neco_a_00934 · Cited by 108
Batch-normalized Maxout Network in NetworkPreprint
Chang, J.-R., & Chen, Y. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.02583 · Cited by 106
An empirical analysis of dropout in piecewise linear networksPreprint
Warde-Farley, D., Goodfellow, I., Courville, A. C., & Bengio, Y. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1312.6197 · Cited by 105
Recover Canonical-View Faces in the Wild with Deep Neural NetworksPreprint
Zhu, Z., Luo, P., Wang, X., & Tang, X. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1404.3543 · Cited by 102
Multi-GPU Training of ConvNetsPreprint
Yadan, O., Adams, K., Taigman, Y., & Ranzato, M. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1312.5853 · Cited by 98
Improving Deep Neural Networks with Probabilistic Maxout UnitsPreprint
Springenberg, J. T., & Riedmiller, M. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1312.6116 · Cited by 94
Proceedings of the 17th international conference on Artificial neural networks
de Sá, J. M., Alexandre, L. A., Duch, W., & Mandic, D. P. (2007) · Cited by 87
GPU Asynchronous Stochastic Gradient Descent to Speed Up Neural Network TrainingPreprint
Paine, T., Jin, H., Yang, J., Lin, Z., & Huang, T. S. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1312.6186 · Cited by 84
Complex cell pooling and the statistics of natural images
Hyvärinen, A., & Köster, U. (2007) · Network Computation in Neural Systems · doi:10.1080/09548980701418942 · Cited by 78
A neural architecture for fast and robust face detection
García, C., & Delakis, M. (2003) · doi:10.1109/icpr.2002.1048232 · Cited by 75
Robust Convolutional Neural Networks under Adversarial NoisePreprint
Jin, J., Dündar, A., & Culurciello, E. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1511.06306 · Cited by 72
Face Detection Using GPU-Based Convolutional Neural Networks
Nasse, F., Thurau, C., & Fink, G. A. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-03767-2_10 · Cited by 71
Identity Matters in Deep LearningPreprint
Hardt, M., & Ma, T. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.04231 · Cited by 69
Random Walk Initialization for Training Very Deep Feedforward NetworksPreprint
Sussillo, D., & Abbott, L. F. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.6558 · Cited by 67
On the importance of normalisation layers in deep learning with piecewise linear activation units
Liao, Z., & Carneiro, G. (2016) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2016.7477624 · Cited by 63
Dense Prediction on Sequences with Time-Dilated Convolutions for Speech RecognitionPreprint
Sercu, T., & Goel, V. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.09288 · Cited by 59
Learning Ordered Representations with Nested DropoutPreprint
Rippel, O., Gelbart, M. A., & Adams, R. P. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1402.0915 · Cited by 55
Cheng, Z., Soudry, D., Mao, Z., & Lan, Z. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1503.03562 · Cited by 53
Signal recovery from Pooling Representations
Estrach, J. B., Szlam, A. D., & LeCun, Y. (2014) · International Conference on Machine Learning · Cited by 51
Nagi, J., Di, G. A., Giusti, A., Nagi, F., & Gambardella, L. M. (2012) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2012.14 · Cited by 50
FPGA‐accelerated deep convolutional neural networks for high throughput and energy efficiency
Qiao, Y., Shen, J., Xiao, T., Yang, Q., Wen, M., & Zhang, C. (2016) · Concurrency and Computation Practice and Experience · doi:10.1002/cpe.3850 · Cited by 50
Visualizing and Comparing Convolutional Neural NetworksPreprint
Yu, W., Yang, K., Bai, Y., Yao, H., & Rui, Y. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.6631 · Cited by 49
Fundamental limits on adversarial robustness
Fawzi, A., Fawzi, O., & Frossard, P. (2015) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 39
A theoretical analysis of Deep Neural Networks for texture classification
Basu, S., Karki, M., Mukhopadhyay, S., Ganguly, S., Nemani, R., DiBiano, R., & Gayaka, S. (2016) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727306 · Cited by 39
Fast Neural Networks with Circulant Projections.Preprint
Cheng, Y., Yu, F. X., Feris, R., Kumar, S., Choudhary, A., & Chang, S.-F. (2015) · arXiv (Cornell University) · Cited by 37
Learnable Pooling Regions for Image ClassificationPreprint
Malinowski, M., & Fritz, M. (2013) · arXiv (Cornell University) · doi:10.48550/arxiv.1301.3516 · Cited by 28
Suppressing the Unusual: towards Robust CNNs using Symmetric Activation FunctionsPreprint
Zhao, Q., & Griffin, L. D. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1603.05145 · Cited by 28
Robustness of Deep Convolutional Neural Networks for Image Recognition
Uličný, M., Lundström, J., & Byttner, S. (2016) · Communications in computer and information science · doi:10.1007/978-3-319-30447-2_2 · Cited by 25
Two-Tier Approach for Arabic Offline Handwriting Recognition
Abdulkader, A. (2006) · Cited by 25
The Scale of Geometric Texture
Oxholm, G., Bariya, P., & Nishino, K. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-33718-5_5 · Cited by 25
DeepHash: Getting Regularization, Depth and Fine-Tuning RightPreprint
Lin, J., Morère, O., Chandrasekhar, V., Veillard, A., & Goh, H. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1501.04711 · Cited by 25
Optimally combining a cascade of classifiers
Chellapilla, K., Shilman, M., & Simard, P. (2006) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.643669 · Cited by 17
Skip Connections as Effective Symmetry-Breaking.Preprint
Orhan, A. E. (2017) · arXiv (Cornell University) · Cited by 15
DNN Flow: DNN Feature Pyramid based Image Matching
Yu, W., Yang, K., Bai, Y., Yao, H., & Rui, Y. (2014) · British Machine Vision Conference (BMVC) · doi:10.5244/c.28.109 · Cited by 14
A gentle Hessian for efficient gradient descent
Collobert, R., & Bengio, S. (2004) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2004.1327161 · Cited by 10
The Loss Surface of Residual Networks: Ensembles and the Role of Batch NormalizationPreprint
Littwin, E., & Wolf, L. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1611.02525 · Cited by 7
A Two-Tier Arabic Offline Handwriting Recognition Based on Conditional Joining Rules
Abdulkader, A. (2008) · Springer eBooks · doi:10.1007/978-3-540-78199-8_5 · Cited by 6
Self-organization of a neural network which gives position-invariant response
Fukushima, K. (1979) · International Joint Conference on Artificial Intelligence · Cited by 5
Flagged first, then by citations
12 of the 286 references couldn’t be matched to a known paper.
Research works printed but not linked: 86
- Abdulkader A., 2006, Proceedings of the 10th International Workshop on Frontiers in Handwriting Recognition, 1
- Arora S., 2014, Proceedings of the 31th International Conference Machine Learning, 584
- Ba J., 2013, Advances in neural information processing systems, 26, 3084
- Ba J., 2015, Proceedings of the 3rd International Conference on Learning Representations, 1
- Bachman P., 2016, Advances in neural information processing systemsm, 29, 4826
- Baldi P., 2013, Advances in neural information processing systems, 26, 3084
- Belkin M., 2006, Journal of Machine Learning Research, 7, 2399
- Bengio Y., 2014, Proceedings of the 31st International Conference Machine Learning, 226
- Bulo S., 2014, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 81
- Chellapilla K., 2006, Proceedings of the 10th International Workshop on Frontiers in Handwriting Recognition.
- Chellapilla K., 2006, Proceedings of the 18th Annual Symposium on Electronic Imaging, 6067
- Chellapilla K., 2006, Proceedings of the 10th Internstional Workshop on Frontiers in Handwriting Recognition, 1
- Choromanska A., 2015, Proceedings 18th International Conference on Artificial Intelligence and Statistics, 192
- Clevert D., 2016, Proceedings of the 4th International Conference on Learning Representations, 1
- Coates A., 2011, Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, 215
- Collobert R., 2006, Journal of Machine Learning Research, 7, 1687
- Courbariaux M., 2015, Advances in neural information processing systems, 28, 3123
- Courbariaux M., 2016, Advances in neural information processing systems, 29, 1
- Deng L., 2014, APSIPA Transactions on Signal and Information Processing, 3, 1
- Denil M., 2013, Advances in neural information processing systems, 26, 2148
- Denton E. L., 2014, Advances in neural information processing systems, 27, 1269
- Dettmers T., 2016, Proceedings of the 4th International Conference on Learning Representations, 1
- Duchi J., 2011, Journal of Machine Learning Research, 12, 2121
- Erhan D., 2010, Journal of Machine Learning Research, 11, 625
- Filho D. P., 2009, Proceedings of the XXII Brazilian Symposium on Computer Graphics and Image Processing, 1
- Fukushima K., 1979, Proceedings of the 6th International Joint Conference on Artificial Intelligence, 291
- Glorot X., 2010, Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, 249
- Glorot X., 2011, Proceedings of the 14th International Conference on Artificial Intelligence and Statistics, 315
- Goodfellow I., 2016, Deep learning
- Goodfellow I., 2014, Advances in neural information processing systems, 27, 2672
- Goodfellow I. J., 2015, Proceedings of the 3rd International Conference on Learning Representations, 1
- Goodfellow I. J., 2013, Proceedings of the 30th International Conference Machine Learning, 1319
- Han S., 2016, Proceedings of the 3rd International Conference on Learning Representations, 1
- Huang G. B., 2007, Labeled faces in the wild: A database for studying face recognition in unconstrained environments
- Iandola F. N., 2016, SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and 1MB model size
- Ioffe S., 2015, Proceedings of the 32nd International Conference Machine Learning, 448
- Jaderberg M., 2015, Advances in neural information processing systems, 28, 2017
- Jin J., 2016, Proceedings of the 4th International Conference on Learning Representations, 1
- Karpathy A., 2016, CS231n: Convolutional neural networks for visual recognition
- Karpathy A., 2016, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 3128
- Kennedy J., 1995, Proceedings of the 6th International Symposium on Micro Machine and Human Science, 39
- Krizhevsky A., 2012, Advances in neural information processing systems, 25, 1097
- Kulkarni T. D., 2015, Advances in neural information processing systemsm, 28, 2539
- Lebedev V., 2014, Speeding-up convolutional neural networks using fine-tuned CP-decomposition
- LeCun Y., 1989, Connections in perspective, 143
- LeCun Y., 1989, Advances in neural information processing systems, 2, 396
- Lee C., 2015, Proceedings of the 18th International Conference on Artificial Intelligence and Statistics, 562
- Leibo J. Z., 2011, Advances in neural information processing systems, 24, 711
- Levine S., 2016, Journal of Machine Learning Research, 17, 1
- Li S., 2016, Demystifying ResNet
- Li Z., 2016, Advances in neural information processing systemsm, 1
- Lin M., 2013, Network in network
- Linnainmaa S., 1970, The representation of the cumulative rounding error of an algorithm as a Taylor expansion of the local rounding errors
- Liu W., 2016, Proceedings of the 33rd International Conference Machine Learning, 507
- Maas A. L., 2013, Proceedings of the 30th International Conference Machine Learning, 1
- Maharaj A. V., 2015, Improving the adversarial robustness of ConvNets by reduction of input dimensionality
- Mishkin D., 2016, Proceedings of the 4th International Conference on Learning Representations, 1
- National Data Science Bowl | Kaggle, 2016, Kaggle.com
- Netzer Y., 2011, Advances in neural information processing systems, 24 (NIPS) Workshop on Deep Learning and Unsupervised Feature Learning, 1
- Novikov A., 2015, Advances in neural information processing systems, 28
- Ranzato M., 2006, Advances in neural information processing systems, 19, 1137
- Recht B., 2011, Advances in neural information processing systems, 24, 693
- Rippel O., 2014, Proceedings of the 30th International Conference on Machine Learning, 1746
- Rippel O., 2015, Advances in neural information processing systems, 28, 2449
- Romero A., 2015, Proceedings of the 3rd International Conference on Learning Representations, 1
- Sabour S., 2016, Proceedings of the 4th International Conference on Learning Representations, 1
- Sermanet P., 2012, Proceedings of the 21st International Conference on Pattern Recognition, 3288
- Snoek J., 2012, Advances in neural information processing systems, 25, 2951
- Sontag E. D., 1998, NATO ASI Series F Computer and Systems Sciences, 168, 69
- Srivastava N., 2014, Journal of Machine Learning Research, 15, 1929
- Srivastava N., 2013, Advances in neural information processing systems, 26, 2094
- Srivastava R. K., 2015, Advances in neural information processing systems, 28, 2377
- Srivastava R. K., 2015, Highway networks
- Steinkrau D., 2005, Proceedings of the 8th International Conference on Document Analysis and Recognition, 1115
- Stollenga M. F., 2014, Advances in neural information processing systems, 27, 3545
- Sun Y., 2014, Advances in neural information processing systems, 27, 1988
- Sutskever I., 2013, Proceedings of the 30th International Conference Machine Learning, 1139
- Szegedy C., 2014, Proceedings of the 1st International Conference on Learning Representations, 1
- Wager S., 2013, Advances in neural information processing systems, 26, 351
- Wan L., 2013, Proceedings of the 30th International Conference Machine Learning, 1058
- Wang S. I., 2013, Proceedings of the 30th International Conference Machine Learning, 118
- Wang Y., 2016, Advances in neural information processing systems, 29, 1
- Weinberger K. Q., 2005, Advances in neural information processing systems, 18, 1473
- Yang J., 2009, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1794
- Zhai S., 2016, Advances in neural information processing systems, 29, 1
- Zinkevich M., 2010, Advances in neural information processing systemsm, 23, 2595
Cited by
Sorted by citations
A Review of Recurrent Neural Networks: LSTM Cells and Network Architectures
Yu, Y., Si, X., Hu, C., & Zhang, J. (2019) · Neural Computation · doi:10.1162/neco_a_01199 · Cited by 5,870
A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects
Li, Z., Liu, F., Yang, W., Peng, S., & Zhou, J. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2021.3084827 · Cited by 5,273
“Rawat and Wang [14] reviewed CNN for image recognition.”
BackgroundResUNet-a: A deep learning framework for semantic segmentation of remotely sensed data
Diakogiannis, F. I., Waldner, F., Caccetta, P. A., & Wu, C. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.01.013 · Cited by 2,295
A State-of-the-Art Survey on Deep Learning Theory and Architectures
Alom, M. Z., Taha, T. M., Yakopcic, C., Westberg, S. K. E., Sidike, P., Nasrin, M. S., Hasan, M., Essen, B. C. V., Awwal, A. A. S., & Asari, V. K. (2019) · Electronics · doi:10.3390/electronics8030292 · Cited by 1,722
Deep learning in spiking neural networks
Tavanaei, A., Ghodrati, M., Kheradpisheh, S. R., Masquelier, T., & Maida, A. S. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.12.002 · Cited by 1,522
“This type of network has shown outstanding performance in image recognition [2], [135],”
Metadata
- Authors
- Rawat, W., & Wang, Z.
- Affiliations
- University of South Africa (ZA)
- ORCID
- Wang, Zenghui
- Venue
- Neural Computation
- Publisher
- MIT Press
- Published
- 2017-09
- Volume
- 29
- Issue
- 9
- Pages
- 2352-2449
- ISSN
- 0899-7667 (ISSN-L)
- 1530-888X
- OpenAlex
- W2622826443
- Cited by
- 3,503
- References
- 286
- Citations by year
- 2026: 181 · 2025: 393 · 2024: 470 · 2023: 535 · 2022: 580 · 2021: 589 · 2020: 432 · 2019: 234 · 2018: 84 · 2017: 3
- FWCI
- 85.59
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Neural Computation (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Keywords
- convolutional neural networks, image classification, deep learning, history of neural networks, annotated dataset, computer vision
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
