The DOI matches its Crossref record, with a small difference in title or year; 31 of the 31 references it cites were found in a registry or an index; 2 of the 14 sources its claims cite were checked for support.
ImageNet classification with deep convolutional neural networks
Open accessAbstract
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 overfitting in the fully connected layers we employed a recently developed regularization method called "dropout" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
42 Own result · 21 Own interpretation · 7 No basis given · 4 No full text of the sources · 3 Sources not found · 2 Supported · 2 Not checked yet · 1 Own method
Every source the paper cites for this claim was checked and supports it.
In the paper
“This technique reduces complex co adaptations of neurons, since a neuron cannot rely on the presence of particular other neurons.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.2. Dropout
What the sources say
- Supportedcites [12] 12. Hinton, G., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012). source
The paper states on p. 1 that dropout prevents complex co-adaptations by randomly omitting hidden units, so a hidden unit cannot rely on other hidden units being present. This directly supports the citing sentence. (LLM, conf=0.95)
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“There is, however, a very efficient version of model combination that only costs about a factor of two during training.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.2. Dropout
What the sources say
- Supportedcites [12] 12. Hinton, G., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R. Improving neural networks by preventing co-adaptation of feature detectors. arXiv preprint arXiv:1207.0580 (2012). source
The paper describes dropout as an efficient way to combine many neural-network models (p. 1). In F.6 Training (p. 17), it reports that ImageNet training took roughly four days with dropout versus two days without it, supporting the approximate factor-of-two training cost. (LLM, conf=0.88)
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This scheme reduces our top-1 and top-5 error rates by 1.7% and 1.2%, respectively, as compared with a net with half as many kernels in each convolutional layer trained on one GPU.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.2. Training on multiple GPUs
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The size of our network made overfitting a significant problem, even with 1.2 million labeled training examples”
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
plainstates a cause- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A result the paper states — it cites no source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“So these data augmentation schemes are, in effect, computationally free.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 5.1. Data augmentation
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- It may state two separate claims as one. 18
- Probably not a substantial claim. 26
- The quote only partly states this claim. 2
Create a free account to see these claims.
Create a free account to see these assumptions.
34 more claims
Create a free account to see all 82 claims and to verify the 3 references still open.
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“In the field of DL, the CNN is the most famous and commonly employed algorithm [30, 71–75].”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [30]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Convolutional neural networks (CNNs), built for spatial data, have demonstrated performance comparable to that of specialists in cancer detection and tumor segmentation [15,16,17,18,19,20].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [16]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Deep CNN architectures with 8, even 22 layers [4], [33], can be useful even for CADe problems where the available training datasets are limited.”
From Deep convolutional neural networks for computer-aided detection: cnn architectures, dataset characteristics and transfer learning (2016) · cites this paper as [4]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“Fully convolutional neural networks (fCNNs) have provided state-of-the-art performance for object classification and image segmentation (1).”
From Fully automated, real-time 3D ultrasound segmentation to estimate first trimester placental volume using deep learning (2018) · cites this paper as [1]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The first wave saw the emergence of deep learning as the dominant paradigm for analyzing high-dimensional biological data.”
From The next paradigm in bioinformatics: a review of multi-agent systems and foundational models for end-to-end scientific discovery (2026) · cites this paper as [1]
Experimental — accuracy not yet measured
19 more claims about this paper
Create a free account to see every claim other papers make about this one.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ ImageNet Classification with Deep Convolutional Neural Networks > 2. INTRODUCTION
“we found that removing any convolutional layer (each of which contains no more than 1% of the model's parameters) resulted in inferior performance.”
§ ImageNet Classification with Deep Convolutional Neural Networks > 4.5. Overall architecture
“The third convolutional layer has 384 kernels of size 3 × 3 × 256”
Citations
Cites
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,884
Self-citationLearning Internal Representations by Error Propagation
Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985) · doi:10.21236/ada164453 · Cited by 16,501
Self-citation(2010) · International Conference on Machine Learning · Cited by 13,995
Self-citationImproving neural networks by preventing co-adaptation of feature detectors
Hinton, G. E., Srivastava, N., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. R. (2012) · arXiv · doi:10.48550/arxiv.1207.0580 · Cited by 6,382
Self-citationFukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759
LabelMe: A Database and Web-Based Tool for Image Annotation
Russell, B. C., Torralba, A., Murphy, K. P., & Freeman, W. T. (2008) · International Journal of Computer Vision · doi:10.1007/s11263-007-0090-8 · Cited by 4,210
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · arXiv · doi:10.48550/arxiv.1512.03385 · Cited by 4,196
Research works printed but not linked: 13
- Berg A., 2010, Large scale visual recognition challenge
- Deng J., 2009, CVPR09
- Krizhevsky A., 2009, Department of Computer Science
- Krizhevsky A., 2010, Convolutional deep belief networks on cifar-10. Unpublished manuscript
- Krizhevsky A., 2011, ESANN
- LeCun Y., 1990, Advances in Neural Information Processing Systems
- LeCun Y., 1985, Une procedure d'apprentissage pour reseau a seuil asymmetrique (a learning scheme for asymmetric threshold networks)
- 10.5555/1896300.1896315 · DOI
- Mensink T., 2012, Italy
- 10.5555/3104322.3104425 · DOI
- Rumelhart D.E., 1985, DTIC Document
- 10.5555/938980.939477 · DOI
- Werbos P., 1974, Beyond regression: New tools for prediction and analysis in the behavioral sciences
Cited by
Items 36,101–36,200 of 108,435
Dental caries diagnosis using neural networks and deep learning: a systematic review
Forouzeshfar, P., Safaei, A. A., Ghaderi, F., Kamangar, S. S. H., Kaviani, H., & Haghi, S. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16599-w · Cited by 21
SWSSL: Sliding Window-Based Self-Supervised Learning for Anomaly Detection in High-Resolution Images
Dong, H., Zhang, Y., Gu, H., Konz, N., Zhang, Y., & Mazurowski, M. A. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3314318 · Cited by 22
OmniBoost: Boosting Throughput of Heterogeneous Embedded Devices under Multi-DNN Workload
Καρατζάς, Α., & Anagnostopoulos, I. (2023) · ACM/IEEE Design Automation Conference (DAC) · doi:10.1109/dac56929.2023.10247989 · Cited by 23
Krag, C. H., Müller, F. C., Gandrup, K. L., Raaschou, H., Andersen, M. B., Brejnebøl, M. W., Sagar, M. V., Bojsen, J. A., Rasmussen, B. S., Graumann, O., et al. (2023) · European Journal of Radiology · doi:10.1016/j.ejrad.2023.111126 · Cited by 25
Hu, A., Yabuki, N., Fukuda, T., Hiroyuki, K. A. G. A., TAKEDA, S., & Matsuo, K. (2023) · Cities · doi:10.1016/j.cities.2023.104562 · Cited by 23
Hamedianfar, A., Laakso, K., Middleton, M., Törmänen, T., Köykkä, J., & Torppa, J. (2023) · Remote Sensing · doi:10.3390/rs15194806 · Cited by 21
Ehtisham, R., Qayyum, W., Camp, C. V., Plevris, V., Mir, J., Khan, Q. U. Z., & Ahmad, A. (2023) · Case Studies in Construction Materials · doi:10.1016/j.cscm.2023.e02530 · Cited by 21
Shen, Y. S., Chen, A., Zhang, X., Zhong, X., Ma, A., Wang, J., Wang, X., Zheng, W., Sun, Y., Yue, L., et al. (2023) · Clinical and Translational Gastroenterology · doi:10.14309/ctg.0000000000000643 · Cited by 22
Intelligent Crack Detection Method Based on GM-ResNet
Li, X., Xu, X., He, X., Wei, X., & Yang, H. (2023) · Sensors · doi:10.3390/s23208369 · Cited by 23
Advances on the morphological classification of radio galaxies: A review
Ndung'u, S., Grobler, T., Wijnholds, S. J., Karastoyanova, D., & Azzopardi, G. (2023) · New Astronomy Reviews · doi:10.1016/j.newar.2023.101685 · Cited by 24
Rapid Processing of Observed Touch through Social Perceptual Brain Regions: An EEG-fMRI Fusion Study
Masson, H. L., & Işık, L. (2023) · Journal of Neuroscience · doi:10.1523/jneurosci.0995-23.2023 · Cited by 24
Tale of Two Cs: Computation vs. Communication Scaling for Future Transformers on Future Hardware
Pati, S., Aga, S., Islam, M., Jayasena, N., & Sinclair, M. D. (2023) · IEEE International Symposium on Workload Characterization · doi:10.1109/iiswc59245.2023.00026 · Cited by 35
Zhou, Z., Qian, X., Hu, J., Chen, G., Zhang, C., Zhu, J., & Dai, Y. (2023) · Quantitative Imaging in Medicine and Surgery · doi:10.21037/qims-23-20 · Cited by 22
Development of a Model for Detection and Grading of Stem Rust in Wheat Using Deep Learning
Nigus, E. A., Taye, G. B., Girmaw, D. W., et al. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-17434-y · Cited by 21
Robust Test Selection for Deep Neural Networks
Sun, W., Yan, M. L., Liu, Z., & Lo, D. (2023) · IEEE Transactions on Software Engineering · doi:10.1109/tse.2023.3330982 · Cited by 24
Counterexample Guided Neural Network Quantization Refinement
Matos, J. B. P., Filho, E. B. D. L., de Bessa, I. V., Manino, E., Song, X., & Cordeiro, L. C. (2023) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2023.3335313 · Cited by 23
Ali, N., Mohammed, A. K., Bais, A., et al. (2023) · Frontiers in Plant Science · doi:10.3389/fpls.2023.1255961 · Cited by 21
Deep Learning-Based Automatic Defect Detection of Additive Manufactured Stainless Steel
Zubayer, M. H., Zhang, C., & Wang, Y. (2023) · Metals · doi:10.3390/met13121987 · Cited by 26
Enhancing neural network classification using fractional-order activation functions
Kumar, M., Mehta, U., & Cirrincione, G. (2023) · AI Open · doi:10.1016/j.aiopen.2023.12.003 · Cited by 21
Personalized Federated Learning with Layer-Wise Feature Transformation via Meta-Learning
Tu, J., Huang, J., Yang, L., & Lin, W. (2023) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3638252 · Cited by 21
Factorization Vision Transformer: Modeling Long-Range Dependency With Local Window Cost
Qin, H., Zhou, D., Xu, T., Bian, Z., & Li, J. (2023) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3342172 · Cited by 25
Suzuki, K., Seth, A. K., & Schwartzman, D. J. (2024) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2023.1159821 · Cited by 26
Developments in deep learning for change detection in remote sensing: A review
Kaur, G., & Afaq, Y. (2024) · Transactions in GIS · doi:10.1111/tgis.13133 · Cited by 28
Sparse optimization guided pruning for neural networks
Shi, Y., Tang, A., Niu, L., & Zhou, R. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.127280 · Cited by 23
Random noise attenuation of 3D multicomponent seismic data using a fast adaptive prediction filter
Wang, Z., Liu, G., Li, C., Shi, L., & Wang, Z. (2024) · Geophysics · doi:10.1190/geo2023-0195.1 · Cited by 25
Deep-learning framework for optimal selection of soil sampling sites
Pham, T.-H., Acharya, P., Bachina, S., Osterloh, K., & Nguyen, K. (2024) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2024.108650 · Cited by 22
Nogales, A., Rodríguez-Aragón, M., & García-Tejedor, Á. J. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108082 · Cited by 27
Decoding brain signals: A convolutional neural network approach for motor imagery classification
Tarahi, O., Hamou, S., Moufassih, M., Agounad, S., & Azami, H. I. (2024) · e-Prime - Advances in Electrical Engineering Electronics and Energy · doi:10.1016/j.prime.2024.100451 · Cited by 23
Sofos, F. D., Drikakis, D., Kokkinakis, I. W., & Spottswood, S. M. (2024) · Physics of Fluids · doi:10.1063/5.0190272 · Cited by 23
Strength in Diversity: Multi-Branch Representation Learning for Vehicle Re-Identification
Almeida, E., Silva, B., & Batista, J. P. (2023) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc57777.2023.10422175 · Cited by 21
Zhang, J., Zhang, R., Yuan, W., & Liu, Y. (2024) · Evolving Systems · doi:10.1007/s12530-024-09567-8 · Cited by 21
Deep Learning for Refined Lithology Identification of Sandstone Microscopic Images
Wang, C., Li, P., Long, Q., et al. (2024) · Minerals · doi:10.3390/min14030275 · Cited by 21
Unraveling the mysteries of AI chatbots
Bridgelall, R. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10720-7 · Cited by 31
Xing, F., An, R., Guo, X., & Shen, X. (2024) · GIScience & Remote Sensing · doi:10.1080/15481603.2024.2327146 · Cited by 21
E2ETCA: End-to-end training of CNN and attention ensembles for rice disease diagnosis
Uddin, M. Z., Mahamood, M. N., Ray, A., et al. (2024) · Journal of Integrative Agriculture · doi:10.1016/j.jia.2024.03.075 · Cited by 21
Sabnam, S., & Rajagopal, S. (2024) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2024.2330524 · Cited by 26
Ghosh, S., Singh, A. J., & Kumar, S. (2024) · Ecological Informatics · doi:10.1016/j.ecoinf.2024.102581 · Cited by 21
Ahamed, M. F., Islam, M. R., Nahiduzzaman, M., Chowdhury, M. E. H., Alqahtani, A., & Murugappan, M. (2024) · Human-Centric Intelligent Systems · doi:10.1007/s44230-024-00067-1 · Cited by 21
CaSnLi-YOLO: construction site multi-target detection method based on improved YOLOv5s
Hao, F., Zhang, T., He, G., Dou, R., & Meng, C. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad42c5 · Cited by 21
Identification of Rare Wildlife in the Field Environment Based on the Improved YOLOv5 Model
Su, X., Zhang, J., Ma, Z., Dong, Y., Zi, J., Xu, N., Zhang, H., Xu, F., & Chen, F. (2024) · Remote Sensing · doi:10.3390/rs16091535 · Cited by 21
High-performance computing in healthcare: An automatic literature analysis perspective
Li, J., Wang, S., Rudinac, S., & Osseyran, A. (2024) · Journal Of Big Data · doi:10.1186/s40537-024-00929-2 · Cited by 24
A Deep Learning Classification Scheme for PolSAR Image Based on Polarimetric Features
Zhang, S., Cui, L., Dong, Z., et al. (2024) · Remote Sensing · doi:10.3390/rs16101676 · Cited by 21
Khan, S. D., & Othman, K. M. (2024) · Computers · doi:10.3390/computers13050121 · Cited by 21
Plant disease identification and pesticides recommendation using Dense Net
Banothu, S., Madhavi, K., Kumar, K. M. V. M., et al. (2024) · Cogent Engineering · doi:10.1080/23311916.2024.2353080 · Cited by 21
EMR-YOLO: A Study of Efficient Maritime Rescue Identification Algorithms
Zhang, J., Hua, Y., Chen, L., Li, L., Shen, X., Shi, W. X., Wu, S., Fu, Y., Lv, C., & Zhu, J. (2024) · Journal of Marine Science and Engineering · doi:10.3390/jmse12071048 · Cited by 24
Deep-Learning-Based Automatic Sinkhole Recognition: Application to the Eastern Dead Sea
AlRabayah, O., Caus, D., Watson, R. A., Schulten, H. Z., Weigel, T., Rüpke, L. H., & Al‐Halbouni, D. (2024) · Remote Sensing · doi:10.3390/rs16132264 · Cited by 23
The Building Blocks of Thought: A Rationalist Account of the Origins of Concepts
Laurence, S., & Margolis, E. (2024) · doi:10.1093/9780191925375.001.0001 · Cited by 26
YOLOv8-G: An Improved YOLOv8 Model for Major Disease Detection in Dragon Fruit Stems
Huang, L., Chen, M., & Peng, Z. (2024) · Sensors · doi:10.3390/s24155034 · Cited by 23
A Generalized Attention Mechanism to Enhance the Accuracy Performance of Neural Networks
Jiang, P., Neri, F., Xue, Y., & Maulik, U. (2024) · International Journal of Neural Systems · doi:10.1142/s0129065724500631 · Cited by 25
Neural network developments: A detailed survey from static to dynamic models
Verma, P. R., Singh, N. P., Pantola, D., & Cheng, X. (2024) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2024.109710 · Cited by 26
Principles of intensive human neuroimaging
Kupers, E. R., Knapen, T. H. J., Merriam, E. P., & Kay, K. (2024) · Trends in Neurosciences · doi:10.1016/j.tins.2024.09.011 · Cited by 25
Gu, Q., Li, T., Hu, Z., et al. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.109960 · Cited by 21
Self-supervised multi-modality learning for multi-label skin lesion classification
Wang, H., Ahn, E., Bi, L., & Kim, J. (2025) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2025.108729 · Cited by 27
Overview of Object Detection Algorithms Using Convolutional Neural Networks
Ren, J., & Wang, Y. (2022) · Journal of Computer and Communications · doi:10.4236/jcc.2022.101006 · Cited by 36
Transfer learning method using multi-prediction deep Boltzmann machines for a small scale dataset
Sawada, Y., & Kozuka, K. (2015) · Machine Vision Applications · doi:10.1109/mva.2015.7153145 · Cited by 22
Age estimation via unsupervised neural networks
Wang, X., & Kambhamettu, C. (2015) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2015.7163119 · Cited by 19
Combination of two-dimensional cochleogram and spectrogram features for deep learning-based ASR
Tjandra, A., Sakti, S., Neubig, G., Toda, T., Adriani, M., & Nakamura, S. (2015) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2015.7178827 · Cited by 23
Modeling the motor cortex: Optimality, recurrent neural networks, and spatial dynamics
Tanaka, H. (2015) · Neuroscience Research · doi:10.1016/j.neures.2015.10.012 · Cited by 25
Deep belief networks for predicting corporate defaults
Yeh, S.-H., Wang, C., & Tsai, M.-F. (2015) · Wireless and Optical Communication Conference (WOCC) · doi:10.1109/wocc.2015.7346197 · Cited by 21
LEWIS: Latent Embeddings for Word Images and Their Semantics
Gordo, A., Almazán, J., Murray, N., et al. (2015) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.147 · Cited by 21
Beyond Tree Structure Models: A New Occlusion Aware Graphical Model for Human Pose Estimation
Fu, L., Zhang, J., & Huang, K. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.229 · Cited by 23
Beyond Classification: Latent User Interests Profiling from Visual Contents Analysis
Yang, L., Hsieh, C.-K., & Estrin, D. L. (2015) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2015.160 · Cited by 21
Wang, H. H., Liu, J., You, M., et al. (2015) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2015.7359724 · Cited by 21
Stacked Multiscale Feature Learning for Domain Independent Medical Image Segmentation
Kiros, R., Popuri, K., Cobzaş, D., & Jägersand, M. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10581-9_4 · Cited by 23
Supervised Greedy Layer-Wise Training for Deep Convolutional Networks with Small Datasets
Rueda-Plata, D., Ramos-Pollán, R., & Gonzalez, F. A. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24069-5_26 · Cited by 21
Accurate Human-Limb Segmentation in RGB-D Images for Intelligent Mobility Assistance Robots
Chandra, S., Tsogkas, S., & Kokkinos, I. (2015) · doi:10.1109/iccvw.2015.64 · Cited by 21
Prajna: Towards Recognizing Whatever You Want from Images without Image Labeling
Hua, X., & Li, J. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v29i1.9186 · Cited by 21
A Unified Framework for Painting Classification
Saleh, B., & Elgammal, A. (2015) · IEEE ... International Conference on Data Mining workshops · doi:10.1109/icdmw.2015.93 · Cited by 22
Content specific feature learning for fine-grained plant classification
Ge, Z., McCool, C., Sanderson, C., et al. (2015) · QUT ePrints (Queensland University of Technology) · Cited by 21
Category co-occurrence modeling for large scale scene recognition
Song, X., Jiang, S., Herranz, L., Kong, Y., & Zheng, K. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.01.019 · Cited by 22
Hauswald, J., Laurenzano, M. A., Zhang, Y., et al. (2016) · ACM Transactions on Computer Systems · doi:10.1145/2870631 · Cited by 21
A knowledge-based recognition system for historical Mongolian documents
Su, X., Gao, G., Wei, H., et al. (2016) · International Journal on Document Analysis and Recognition (IJDAR) · doi:10.1007/s10032-016-0267-1 · Cited by 21
Clustering of cell populations in flow cytometry data using a combination of Gaussian mixtures
Reiter, M., Rota, P., Kleber, F., Diem, M., Groeneveld‐Krentz, S., & Dworzak, M. N. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.04.004 · Cited by 20
Part-based room categorization for household service robots
Peter, U., Mandeljc, R., Leonardis, A., & Kristan, M. (2016) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2016.7487377 · Cited by 22
Thin-Slicing for Pose: Learning to Understand Pose without Explicit Pose Estimation
Kwak, S., Cho, M., & Laptev, I. (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.2016.534 · Cited by 19
Large Scale Indexing and Searching Deep Convolutional Neural Network Features
Amato, G. M., Debole, F., Falchi, F., Gennaro, C., & Rabitti, F. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-43946-4_14 · Cited by 21
Semantic Feature Mining for Video Event Understanding
Yang, X., Zhang, T., & Xu, C. (2016) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/2962719 · Cited by 21
Face segmentation in thumbnail images by data-adaptive convolutional segmentation networks
Kıranyaz, S., Waris, M. A., Ahmad, I. A., et al. (2016) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2016.7532770 · Cited by 21
Multi-Modal Representations for Improved Bilingual Lexicon Learning
Vulić, I., Kiela, D., Clark, S. C., & Moens, M. (2016) · Lirias · doi:10.17863/cam.9719 · Cited by 21
Feature Learning and Deep Learning Architecture Survey
Krig, S. (2016) · Springer eBooks · doi:10.1007/978-3-319-33762-3_10 · Cited by 22
Moving towards in object recognition with deep learning for autonomous driving applications
Uçar, A., Demir, Y., & Guzelis, C. (2016) · International Conference on Innovations in Intelligent Systems and Applications (INISTA) · doi:10.1109/inista.2016.7571862 · Cited by 21
YFCC100M-HNfc6: A Large-Scale Deep Features Benchmark for Similarity Search
Amato, G. M., Falchi, F., Gennaro, C., & Rabitti, F. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46759-7_15 · Cited by 21
Deep Permutations: Deep Convolutional Neural Networks and Permutation-Based Indexing
Amato, G. M., Falchi, F., Gennaro, C., & Vadicamo, L. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46759-7_7 · Cited by 22
Zhao, L., Wang, Z., Wang, X., Qi, Y., Liu, Q., & Zhang, G. (2016) · Journal of Electronic Imaging · doi:10.1117/1.jei.25.5.053024 · Cited by 21
Real-time person detection in low-resolution thermal infrared imagery with MSER and CNNs
Herrmann, C., Müller, T., Willersinn, D., & Beyerer, J. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2240940 · Cited by 26
Classifying objects in LWIR imagery via CNNs
Rodger, I., Connor, B., & Robertson, N. M. (2016) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2241858 · Cited by 21
Genre classification of paintings
Cetinić, E., & Grgić, S. (2016) · International Symposium ELMAR · doi:10.1109/elmar.2016.7731786 · Cited by 21
Wang, K., Guo, P., Yin, Q., et al. (2016) · doi:10.1109/ijcnn.2016.7727642 · Cited by 21
Optimizing neural-network learning rate by using a genetic algorithm with per-epoch mutations
Kanada, Y. (2016) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2016.7727372 · Cited by 25
Report on the BTAS 2016 Video Person Recognition Evaluation
Scheirer, W. J., Flynn, P. J., Ding, C., Guo, G., Štruc, V., Jazaery, M. A., Grm, K., Dobrišek, S., Tao, D., Zhu, Y. M., et al. (2016) · IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS) · doi:10.1109/btas.2016.7791198 · Cited by 20
Complex Event Recognition from Images with Few Training Examples
Ahsan, U., Sun, C., Hays, J. H., & Essa, I. A. (2017) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2017.80 · Cited by 23
Move prediction in Gomoku using deep learning
Shao, K., Zhao, D., Tang, Z., & Zhu, Y. (2016) · Youth Academic Annual Conference of Chinese Association of Automation (YAC) · doi:10.1109/yac.2016.7804906 · Cited by 20
Zhong, Z., Pan, W., Jin, L., Mouchère, H., & Viard-Gaudin, C. (2016) · International Conference on Frontiers in Handwriting Recognition · doi:10.1109/icfhr.2016.0063 · Cited by 21
Compression of Deep Neural Networks for Image Instance Retrieval
Chandrasekhar, V., Lin, J., Liao, Q., et al. (2017) · DCC · doi:10.1109/dcc.2017.93 · Cited by 21
Deep belief networks and stacked autoencoders for the P300 Guilty Knowledge Test
Kulasingham, J. P., Vibujithan, V., & Silva, A. C. D. (2016) · IEEE-EMBS Conference on Biomedical Engineering and Sciences · doi:10.1109/iecbes.2016.7843428 · Cited by 20
Friend or foe: exploiting sensor failures for transparent object localization and classification
Seib, V., Barthen, A., Marohn, P., et al. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2266255 · Cited by 21
Heterogeneous team deep q-learning in low-dimensional multi-agent environments
Kurek, M., & Jaśkowski, W. (2016) · IEEE Conference on Computational Intelligence and Games (CIG) · doi:10.1109/cig.2016.7860413 · Cited by 21
Leprosy lesion recognition using convolutional neural networks
Baweja, H. S., & Parhar, T. (2016) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc.2016.7860891 · Cited by 23
Deep model-based feature extraction for predicting protein subcellular localizations from bio-images
Shao, W., Ding, Y., Shen, H., et al. (2017) · Frontiers of Computer Science · doi:10.1007/s11704-017-6538-2 · Cited by 21
High-Performance Kernel Machines With Implicit Distributed Optimization and Randomization
Avron, H., & Sindhwani, V. (2015) · Technometrics · doi:10.1080/00401706.2015.1111261 · Cited by 21
Metadata
- Authors
- Krizhevsky, A., Sutskever, I., & Hinton, G. E.
- Affiliations
- Google (United States) (US)
- University of Toronto (CA)
- Venue
- Communications of the ACM
- Published
- 2017-05-24
- Volume
- 60
- Issue
- 6
- Pages
- 84-90
- ISSN
- 0001-0782 (ISSN-L)
- 1557-7317
- OpenAlex
- W2163605009
- Cited by
- 112,155
- References
- 27
- Citations by year
- 2026: 2710 · 2025: 7710 · 2024: 10211 · 2023: 12267 · 2022: 12280 · 2021: 15497 · 2020: 14538 · 2019: 13976 · 2018: 9999 · 2017: 6661
- FWCI
- 5127.18
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Communications of the ACM (publishedVersion)
- neural information processing systems
- 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
- ImageNet, image classification, deep convolutional neural networks, dropout regularization, overfitting
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.
A language model drew the claims from the paper on 2026-10-11, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-11 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
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
