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 49,601–49,700 of 108,439
Sajid, M., Malik, K. R., Rehman, A. U., Malik, T. S., Alajmi, M., Khan, A. H., Haider, A., & Hussen, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-87376-y · Cited by 11
Fuzzy attention-based deep neural networks for acute lymphoblastic leukemia diagnosis
Zhang, T., & Xue, G. (2025) · Applied Soft Computing · doi:10.1016/j.asoc.2025.112810 · Cited by 11
Remote Sensing Cross-Modal Text-Image Retrieval Based on Attention Correction and Filtering
Yang, X., Li, C., Wang, Z., Xie, H., Mao, J., & Yin, G. (2025) · Remote Sensing · doi:10.3390/rs17030503 · Cited by 13
Peng, M., Tang, H., & Kou, Y. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110156 · Cited by 11
Chinga, A., Bendezu, W., & Angulo, A. (2025) · doi:10.3390/engproc2025083022 · Cited by 12
ReSBM: Region-based Scale and Minimal-Level Bootstrapping Management for FHE via Min-Cut
Liu, Y., Lai, J., Li, L., et al. (2025) · ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3669940.3707276 · Cited by 11
Patel, A. N., & Srinivasan, K. (2025) · Physica Medica · doi:10.1016/j.ejmp.2025.104914 · Cited by 13
Yilmaz, E., Görürgöz, C., Kış, H. C., Canger, E. M., & Öztaş, B. (2025) · Forensic Science Medicine and Pathology · doi:10.1007/s12024-025-00962-4 · Cited by 12
The Lightweight Deep Learning Model in Sunflower Disease Identification: A Comparative Study
Zhang, L., & Wu, X. (2025) · Applied Sciences · doi:10.3390/app15042104 · Cited by 11
Engel, D., Greulich, R. S., Parola, A., Vinehout, K., Student, J., Waldthaler, J., Timmermann, L., Bremmer, F., & Bremmer, F. (2025) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-025-01570-7 · Cited by 14
A Comprehensive Evaluation of Monocular Depth Estimation Methods in Low-Altitude Forest Environment
Jia, J., Kang, J., Chen, L., Gao, X., Zhang, B., & Yang, G. (2025) · Remote Sensing · doi:10.3390/rs17040717 · Cited by 12
AI-enabled routing in next generation networks: A survey
Aktas, F., Shayea, I. A. M., Ergen, M., Saoud, B., Yahya, A. E., & Aldasheva, L. (2025) · Alexandria Engineering Journal · doi:10.1016/j.aej.2025.01.095 · Cited by 21
Guo, Y., Wang, H., Ren, X., Wang, T., Chen, W., Xu, Z., & Ge, H. (2025) · Journal of Evidence-Based Medicine · doi:10.1111/jebm.70004 · Cited by 11
Winetrout, J. J., Li, Z., Zhao, Q., Gaber, L., Unnikrishnan, V. U., Varshney, V., Xu, Y., Wang, Y., & Heinz, H. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2415068122 · Cited by 12
Open-Set Fabric Defect Detection With Defect Generation and Transfer
Gao, C., Chen, X., Zhou, J., Wang, J., & Shen, L. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3547485 · Cited by 11
SignEdgeLVM transformer model for enhanced sign language translation on edge devices
Damdoo, R., & Kumar, P. (2025) · Discover Computing · doi:10.1007/s10791-025-09509-1 · Cited by 19
Wang, Y., Zhao, Y.-P., Liang, B.-Y., et al. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110510 · Cited by 11
Komeh, Z., Hamzeh, S., Memarian, H., Attarchi, S., & Alavipanah, S. K. (2025) · International Journal of Environmental Research · doi:10.1007/s41742-025-00752-4 · Cited by 11
EmMixformer: Mix Transformer for Eye Movement Recognition
Qin, H., Zhu, H., Jin, X., Song, Q., El‐Yacoubi, M. A., & Gao, X. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3551452 · Cited by 21
Debsarkar, S. S., Aronow, B. J., & Prasath, V. B. S. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110072 · Cited by 15
Man, Z., Guo, Z., Zhou, J., Jiang, H., Liu, D., Lei, Z., Bai, R., Yang, Z., Yang, T., Chen, M., et al. (2025) · Optics & Laser Technology · doi:10.1016/j.optlastec.2025.112874 · Cited by 12
Parameter Disentanglement for Diverse Representations
Wang, J., Guo, J., Wang, R., Zhang, Z., Fu, L., & Ye, Q. (2025) · Big Data Mining and Analytics · doi:10.26599/bdma.2024.9020087 · Cited by 11
Tree-based Models for Vertical Federated Learning: A Survey
Qian, B., Xie, Y., Li, Y., Ding, B., & Zhou, J. (2025) · ACM Computing Surveys · doi:10.1145/3728314 · Cited by 16
Φ -OTDR Event Recognition System Based on Reconstructed Signal Feature Space
Chen, J., Shi, Y., Wei, C., & Yu, Y. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2025.3558470 · Cited by 11
Gao, Q., Wu, T., Tang, H., et al. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3559939 · Cited by 11
Chien, C.-H., & Trappey, A. J. C. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103327 · Cited by 11
Street View Imagery in Traffic Crash and Road Safety Analysis: A Review
Ye, X., Li, S., Gong, W., Li, X., Li, X., Dadashova, B., Li, W., Du, J., & Wu, D. (2025) · Applied Spatial Analysis and Policy · doi:10.1007/s12061-025-09653-7 · Cited by 15
Bilingual Sign Language Recognition: A YOLOv11-Based Model for Bangla and English Alphabets
Navin, M. N., Farid, F. A., Rakin, R. Z., et al. (2025) · Journal of Imaging · doi:10.3390/jimaging11050134 · Cited by 11
Han, Y., Liu, T., & Li, K. (2025) · Structural Health Monitoring · doi:10.1177/14759217251333761 · Cited by 11
LBA-YOLO: A novel lightweight approach for detecting micro-cracks in building structures
Ren, W., & Zhong, Z. (2025) · PLoS ONE · doi:10.1371/journal.pone.0321640 · Cited by 11
MDFormer: Transformer-Based Multimodal Fusion for Robust Chest Disease Diagnosis
Liu, X., Pan, F., Song, H., Cao, S., Li, C., & Li, T. (2025) · Electronics · doi:10.3390/electronics14101926 · Cited by 16
Verma, P. R., Pantola, D., & Singh, N. P. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110937 · Cited by 13
YOLO-HPSD: A high-precision ship target detection model based on YOLOv10
Zhu, M., Han, D., Han, B., et al. (2025) · PLoS ONE · doi:10.1371/journal.pone.0321863 · Cited by 11
A coordinate transformation-based physics-informed neural networks for hyperbolic conservation laws
Chen, Y., Gao, Z. D., Hesthaven, J. S., Lin, Y., & Sun, X. (2025) · Journal of Computational Physics · doi:10.1016/j.jcp.2025.114161 · Cited by 16
Du, Z., Wu, J., Leng, Y., & Wandelt, S. (2025) · Journal of the Air Transport Research Society · doi:10.1016/j.jatrs.2025.100077 · Cited by 14
Liu, X., Wang, D., Guo, M., et al. (2025) · Geoderma · doi:10.1016/j.geoderma.2025.117396 · Cited by 11
Repurposing of TV boxes for a circular economy in smart cities applications
da Luz, G. P. C. P., Sato, G. M., Gonzalez, L. F. G., & Borin, J. F. (2025) · Scientific Reports · doi:10.1038/s41598-025-97379-4 · Cited by 11
Unrolled sparse coding via lifting wavelet for interpretable intelligent fault diagnosis
Wang, S.-A., Wang, S., Wang, W., Zheng, Y., Ding, B., Yan, R., & Chen, X. (2025) · Reliability Engineering & System Safety · doi:10.1016/j.ress.2025.111371 · Cited by 11
Deep Learning Techniques for Retinal Layer Segmentation to Aid Ocular Disease Diagnosis: A Review
Quintana-Quintana, O. J., Aceves-Fernández, M. A., Pedraza‐Ortega, J. C., Alfonso-Francia, G., & Tovar‐Arriaga, S. (2025) · Computers · doi:10.3390/computers14080298 · Cited by 14
“Initially inspired by the biological visual cortex, CNNs have proven highly effective in tasks like image classification, achieving groundbreaking results in competitions like ImageNet [33].”
Improved Binary Classification of Underwater Images Using a Modified ResNet-18 Model
Mehrunnisa, M., Leszczuk, M., Juszka, D., et al. (2025) · Electronics · doi:10.3390/electronics14152954 · Cited by 11
LEAD-YOLO: A Lightweight and Accurate Network for Small Object Detection in Autonomous Driving
Yang, Y., Yang, S., & Chan, Q. (2025) · Sensors · doi:10.3390/s25154800 · Cited by 11
“The [1, 3, 5] configuration designed in this paper (Figure 9c) not only expands the receptive field size but also has smoother step changes between layers, making the middle layer features (second layer) more balanced in obtaining context information, with stronger structural symmetry and…”
InfluentialVillena, F., Véliz, C., García-Huidobro, R., & Aguayo, S. D. (2025) · Dentistry Review · doi:10.1016/j.dentre.2025.100160 · Cited by 28
Improving deep neural network ensembles using reconstruction error
Huang, W., Hong, H., Bian, K., Zhou, X., Song, G., & Xie, K. (2015) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2015.7280524 · Cited by 11
Computationally bounded retrieval
Rastegari, M., Keskin, C., Kohli, P., & Izadi, S. (2015) · 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.2015.7298757 · Cited by 11
Deep Semantic Pyramids for Human Attributes and Action Recognition
Khan, F. S., Anwer, R. M., van de Weijer, J., Felsberg, M., & Laaksonen, J. T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19665-7_28 · Cited by 10
Chen, L., Wu, C., Fan, W., et al. (2014) · Communications in computer and information science · doi:10.1007/978-3-662-45643-9_48 · Cited by 11
Accelerated Artificial Neural Networks on FPGA for Fault Detection in Automotive Systems
Shreejith, S., Anshuman, B., & Fahmy, S. A. (2016) · Design, Automation, and Test in Europe · doi:10.3850/9783981537079_0337 · Cited by 11
MultiVCRank With Applications to Image Retrieval
Li, X., Ye, Y., & Ng, M. K. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2522298 · Cited by 10
Arabic speech synthesis and diacritic recognition
Rebai, I., & BenAyed, Y. (2016) · International Journal of Speech Technology · doi:10.1007/s10772-016-9342-8 · Cited by 11
Convolutional Neural Networks for Medical Clustering
Lyndon, D., Kumar, A., Kim, J., Leong, P. H. W., & Feng, D. D. (2015) · CLEF (Working Notes) · Cited by 11
Zhang, G., Kato, J., Wang, Y., et al. (2015) · IAPR Asian Conference on Pattern Recognition (ACPR) · doi:10.1109/acpr.2015.7486549 · Cited by 11
Identifying Same Persons from Temporally Synchronized Videos Taken by Multiple Wearable Cameras
Zheng, K., Guo, H., Fan, X., et al. (2016) · IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2016.106 · Cited by 11
Action recognition with spatio-temporal augmented descriptor and fusion method
Li, L., & Dai, S. (2016) · Multimedia Tools and Applications · doi:10.1007/s11042-016-3789-0 · Cited by 11
Deep Active Learning for Autonomous Navigation
Hussein, A. M., Gaber, M. M., & Elyan, E. (2016) · Communications in computer and information science · doi:10.1007/978-3-319-44188-7_1 · Cited by 11
Regression Facial Attribute Classification via simultaneous dictionary learning
Moeini, A., Moeini, H., Safai, A. M., et al. (2016) · Pattern Recognition · doi:10.1016/j.patcog.2016.08.031 · Cited by 11
Learning to Classify Fine-Grained Categories with Privileged Visual-Semantic Misalignment
Chen, K., & Zhang, Z. (2016) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2016.2602231 · Cited by 11
Recognition from Hand Cameras: A Revisit with Deep Learning
Chan, C.-S., Chen, S.-Z., Xie, P.-X., Chang, C., & Sun, M. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46493-0_31 · Cited by 11
Similarity Searching in Long Sequences of Motion Capture Data
Sedmidubský, J., Eliáš, P., & Zezula, P. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46759-7_21 · Cited by 11
CogKnife: Food recognition from their cutting sounds
Kojima, T., Ijiri, T., White, J. S., Kataoka, H., & Hirabayashi, A. (2016) · IEEE International Conference on Multimedia & Expo Workshops (ICMEW) · doi:10.1109/icmew.2016.7574741 · Cited by 11
Thorax disease diagnosis using deep convolutional neural network
Chen, J., Qi, X., Tervonen, O. A., Sílven, O., Zhao, G., & Pietikäinen, M. (2016) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2016.7591186 · Cited by 11
Shortlist Selection with Residual-Aware Distance Estimator for K-Nearest Neighbor Search
Heo, J., Lin, Z., Shen, X., Brandt, J., & Yoon, S. (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.2016.221 · Cited by 9
Real-time classification of vehicles by type within infrared imagery
Kundegorski, M. E., Akçay, S., Garanderie, G. P. D. L., & Breckon, T. P. (2016) · Optics and Photonics for Counterterrorism, Crime Fighting, and Defence · doi:10.1117/12.2241106 · Cited by 11
GPGPU Accelerated Deep Object Classification on a Heterogeneous Mobile Platform
Rizvi, S. T. H., Cabodi, G., Patti, D., et al. (2016) · Electronics · doi:10.3390/electronics5040088 · Cited by 11
Tashiro, S., Nakamura, Y., Matsuda, K., & Matsuoka, M. (2016) · IEEE ... International Conference on Cloud Computing · doi:10.1109/cloud.2016.0092 · Cited by 12
Indoor Positioning Algorithm Based on Parallel Multilayer Neural Network
Dai, H., Liu, H., Xing, X., et al. (2016) · doi:10.1109/isai.2016.0082 · Cited by 11
Weakly-supervised vehicle detection and classification by convolutional neural network
Jiang, C., & Zhang, B. (2016) · International Congress on Image and Signal Processing (CISP) · doi:10.1109/cisp-bmei.2016.7852775 · Cited by 11
Image Semantic Segmentation Based on Fully Convolutional Neural Network and CRF
Li, H., Qian, X., & Li, W. (2017) · Communications in computer and information science · doi:10.1007/978-981-10-3966-9_27 · Cited by 11
Sentiment Recognition in Egocentric Photostreams
Talavera, E., Strisciuglio, N., Petkov, N., & Radeva, P. I. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-58838-4_52 · Cited by 12
Incremental Cross-Modality deep learning for pedestrian recognition
Pop, D. O., Rogozan, A., Nashashibi, F., & Bensrhair, A. (2017) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2017.7995771 · Cited by 13
Collaborative strategy for visual object tracking
Yang, Y., Chen, N., & Jiang, S. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4633-x · Cited by 11
PLSNet: A simple network using Partial Least Squares regression for image classification
Hasegawa, R., & Hotta, K. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7899865 · Cited by 13
Captioning Videos Using Large-Scale Image Corpus
Du, X., Yang, Y., Liu, Y., et al. (2017) · Journal of Computer Science and Technology · doi:10.1007/s11390-017-1738-7 · Cited by 11
Zlateski, A., & Seung, H. S. (2017) · International Conference on Supercomputing · doi:10.1145/3079079.3079081 · Cited by 10
Audio-Replay Attack Detection Countermeasures
Lavrentyeva, G., Novoselov, S., Malykh, E., Kozlov, A., Kudashev, O., & Shchemelinin, V. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-66429-3_16 · Cited by 11
Small Sample Deep Learning for Newborn Gestational Age Estimation
Torres, M. T., Valstar, M., Henry, C., Ward, C., & Sharkey, D. (2017) · International Conference on Automatic Face and Gesture Recognition · doi:10.1109/fg.2017.19 · Cited by 12
A multi-label classification model using convolutional netural networks
Zhang, G., Chen, L., & Ding, Y. (2017) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2017.7978871 · Cited by 11
A two-stage convolutional sparse prior model for image restoration
Xiong, J., Liu, Q., Wang, Y., & Xu, X. (2017) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2017.07.002 · Cited by 11
Fluid region segmentation in OCT images based on convolution neural network
Liu, D., Liu, X., Fu, T., et al. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2282513 · Cited by 11
Unsupervised incremental learning of deep descriptors from video streams
Pernici, F., & Bimbo, A. D. (2017) · IEEE International Conference on Multimedia & Expo Workshops (ICMEW) · doi:10.1109/icmew.2017.8026276 · Cited by 10
Spectral embedding-based multiview features fusion for content-based image retrieval
Feng, L., Yu, L., & Zhu, H. J. (2017) · Journal of Electronic Imaging · doi:10.1117/1.jei.26.5.053002 · Cited by 11
Biomedical Texture Operators and Aggregation Functions
Depeursinge, A., & Fageot, J. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-812133-7.00003-x · Cited by 12
Learning Visual Features from Snapshots for Web Search
Fan, Y., Guo, J., Lan, Y., Xu, J., Pang, L., & Cheng, X. (2017) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3132847.3132943 · Cited by 11
Facial smile detection using convolutional neural networks
Sang, D. V., Cuong, L. T. B., & Thuan, D. P. (2017) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse.2017.8119448 · Cited by 11
Hand gesture recognition using 3D sensors
Goga, J., & Kajan, S. (2017) · International Symposium ELMAR · doi:10.23919/elmar.2017.8124463 · Cited by 11
Avrutskiy, V. I. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-04743-8 · Cited by 21
Neural regularization jointly involving neurons and connections for robust image classification
Lim, G. H., Pedrosa, E., Amaral, F., Lau, N., Pereira, A., Azevedo, J. L., & Cunha, B. (2017) · IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI) · doi:10.1109/mfi.2017.8170451 · Cited by 11
Rare Chinese character recognition by Radical Extraction Network
Yan, Z., Yan, C., & Zhang, C. (2017) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2017.8122728 · Cited by 11
Constrained Convolutional Sparse Coding for Parametric Based Reconstruction of Line Drawings
Shaheen, S. M., Affara, L., & Ghanem, B. (2017) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.474 · Cited by 11
Towards a Unified Compositional Model for Visual Pattern Modeling
Tang, W., Yu, P., Zhou, J., & Wu, Y. N. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.303 · Cited by 11
Automatic nasopharyngeal carcinoma segmentation in MR images with convolutional neural networks
Ma, Z., Wu, X., & Zhou, J. (2017) · International Conference on the Frontiers and Advances in Data Science (FADS) · doi:10.1109/fads.2017.8253215 · Cited by 10
Simplifying very deep convolutional neural network architectures for robust speech recognition
Rownicka, J., Renals, S. J., & Bell, P. (2017) · IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU) · doi:10.1109/asru.2017.8268941 · Cited by 10
Offline Chinese Signature Verification Based on AlexNet
Wencheng, C., Guo, X., Hong, S., et al. (2018) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-319-73317-3_5 · Cited by 11
Unsupervised Video Hashing via Deep Neural Network
Ma, C., Gu, Y., Gong, C., Yang, J., & Feng, D. (2018) · Neural Processing Letters · doi:10.1007/s11063-018-9812-x · Cited by 12
Lapped convolutional neural networks for embedded systems
Wang, X., Ng, H. W., & Liang, J. (2017) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip.2017.8309138 · Cited by 11
Qiu, Y., Du, Y., Zhang, R., Zargari, A., Thai, T., Gunderson, C. C., Moxley, K. M., Liu, H., & Zheng, B. (2018) · SPIE Medical Imaging: Digital and Computational Pathology · doi:10.1117/12.2292840 · Cited by 11
Relative location prediction in CT scan images using convolutional neural networks
Guo, J., Du, H., Zhu, J., Yan, T., & Qiu, B. (2018) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2018.03.025 · Cited by 10
Driver behavioral cloning using deep learning
Kocić, J., Jovicic, N. S., & Drndarević, V. (2018) · International Symposium INFOTEH-JAHORINA (INFOTEH) · doi:10.1109/infoteh.2018.8345542 · Cited by 11
Can Convolution Neural Network (CNN) Triumph in Ear Recognition of Uniform Illumination Invariant?
Jamil, N., Almisreb, A. A., Ariffin, S. M. Z. S. Z., Din, N. M., & Hamzah, R. (2018) · Indonesian Journal of Electrical Engineering and Computer Science · doi:10.11591/ijeecs.v11.i2.pp558-566 · Cited by 11
Cyclic Reservoir Computing with FPGA Devices for Efficient Channel Equalization
Skibinsky-Gitlin, E. S., Alomar, M. L., Frasser, C. F., et al. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-91253-0_22 · Cited by 11
Structure-Aware Multimodal Feature Fusion for RGB-D Scene Classification and Beyond
Wang, A., Cai, J., Lu, J., & Cham, T. (2018) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3115932 · Cited by 10
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
