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 67,501–67,600 of 108,435
Convolutional Gate Recurrent Unit for Video Facial Expression Recognition in the Wild
Kang, K., & Ma, X. (2019) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2019.8866235 · Cited by 7
Multimodal Deep Learning for Solar Irradiance Prediction
Li, Z., Wang, K., Li, C., Zhao, M., & Cao, J. (2019) · IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) · doi:10.1109/ithings/greencom/cpscom/smartdata.2019.00144 · Cited by 5
Chinese Text Feature Extraction and Classification Based on Deep Learning
Wang, R., Li, Z., Cao, J., & Chen, T. (2019) · International Conference on Computer Science and Application Engineering (CSAE) · doi:10.1145/3331453.3361636 · Cited by 7
Uyanik, C., Hussaini, S. F., Erdemir, E., Kaplanoğlu, E., & Sekmen, A. (2019) · Procedia Computer Science · doi:10.1016/j.procs.2019.09.023 · Cited by 7
Does Pooling Really Matter? An Evaluation on Gait Recognition
dos Santos, C. F. G., Moreira, T. P., Colombo, D., & Papa, J. P. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-33904-3_71 · Cited by 5
Machine Learning + Distributed IoT = Edge Intelligence
Wolf, M. C. (2019) · IEEE International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs.2019.00170 · Cited by 5
Compatibility Modeling: Data and Knowledge Applications for Clothing Matching
Song, X., Nie, L., & Wang, Y. (2019) · Synthesis lectures on information concepts, retrieval, and services · doi:10.2200/s00952ed1v01y201909icr069 · Cited by 4
Homogeneous Transformation Based on Deep-Level Features in Heterogeneous Remote Sensing Images
Jiang, X., Li, G., Liu, Y., et al. (2019) · IEEE International Geoscience and Remote Sensing Symposium (IGARSS) · doi:10.1109/igarss.2019.8899123 · Cited by 5
Deep Learning in Computational Neuroscience
Saxena, S., Paul, S., Garg, A., Saikia, A., & Datta, A. (2019) · Advances in computer and electrical engineering book series · doi:10.4018/978-1-7998-0182-5.ch002 · Cited by 5
Nakjai, P., Ponsawat, J., & Katanyukul, T. (2019) · doi:10.1145/3357254.3357266 · Cited by 5
Classification of Field-Level Crop Types with a Time Series Satellite Data Using Deep Neural Network
Jayanth, J., Shalini, V. S., Kumar, T. A., & Koliwad, S. (2019) · Remote sensing and digital image processing · doi:10.1007/978-3-030-24178-0_3 · Cited by 6
Photo Filter Classification and Filter Recommendation without Much Manual Labeling
Chu, W.-T., & Fan, Y.-T. (2019) · IEEE International Workshop on Multimedia Signal Processing (MMSP) · doi:10.1109/mmsp.2019.8901831 · Cited by 5
Jabbar, A., Mendes, A. S., & Chalup, S. K. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34995-0_29 · Cited by 5
CNN-based Camera Model Identification Using Image Noise in Frequency Domain
Cai, T., Shao, Z., Tomioka, Y., et al. (2019) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc.2019.8914375 · Cited by 5
Smart Grids False Data Injection Identification: a Deep Learning Approach
Alves, H. D. N., Bretas, N. G., Bretas, A. S., et al. (2019) · IEEE PES Innovative Smart Grid Technologies Europe · doi:10.1109/isgteurope.2019.8905547 · Cited by 5
Prediction Of Personality Traits From Videos By Using Machine Learning Algorithms
Taş, E., & Kamaşak, M. E. (2019) · doi:10.1109/ubmk.2019.8907179 · Cited by 5
Learning Spatial-Aware Cross-View Embeddings for Ground-to-Aerial Geolocalization
Cao, R., Zhu, J., Li, Q., Zhang, Q., Li, Q., Liu, B., & Qiu, G. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-34120-6_5 · Cited by 5
Anomalous Behaviour Detection using Transfer Learning Algorithm of Series and DAG Network
Razak, H. A., Almisreb, A. A., Saleh, M. A., et al. (2019) · IEEE International Conference on System Engineering and Technology (ICSET) · doi:10.1109/icsengt.2019.8906470 · Cited by 5
Deep Ego-Motion Classifiers for Compound Eye Cameras
Yoo, H., Cha, G., & Oh, S. (2019) · Sensors · doi:10.3390/s19235275 · Cited by 5
Channel Max Pooling for Image Classification
Lu, C., Chang, D., Xie, J., Ma, R., Wu, C., & Ma, Z. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-36189-1_23 · Cited by 6
Betel Nut Classification Method Based on Transfer Learning
Cai, H., & Liu, S. (2019) · IEEE Data Driven Control and Learning Systems Conference (DDCLS) · doi:10.1109/ddcls.2019.8909008 · Cited by 5
Using Convolutional Neural Network in Cross-Domain Argumentation Mining Framework
Bouslama, R., Ayachi, R., & Amor, N. B. (2019) · Scalable Uncertainty Management · doi:10.1007/978-3-030-35514-2_26 · Cited by 2
Implementation of 1D-Convolution Neural Network for Pneumonia Classification Based Chest X-Ray Image
Fathurahman, M., Fauzi, S. C., Haryanti, S. C., Rachmawati, U. A., & Suherlan, E. (2019) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-36056-6_18 · Cited by 5
Robust Visual Tracking via Multilayer CaffeNet Features and Improved Correlation Filtering
Xiao, Y., & Pan, D.-F. (2019) · IEEE Access · doi:10.1109/access.2019.2957518 · Cited by 5
Object Recognition System using Deep Learning with Depth Images for Service Robots
Yoshimoto, Y., & Tamukoh, H. (2018) · International Symposium on Intelligent Signal Processing and Communication Systems (ISPACS) · doi:10.1109/ispacs.2018.8923533 · Cited by 5
Image Quality Detection Using The Siamese Convolutional Neural Network
Vizvary, L., Sopiak, D., Oravec, M., et al. (2019) · International Symposium ELMAR · doi:10.1109/elmar.2019.8918678 · Cited by 5
On pruned, quantized and compact CNN architectures for vision applications
Gadosey, P. K., Li, Y., & Yamak, P. T. (2019) · International Conference on Artificial Intelligence, Information Processing and Cloud Computing (AIIPCC) · doi:10.1145/3371425.3371481 · Cited by 5
Data Driven Hourly Taxi Drop-offs Prediction using TLC Trip Record Data
Wickramasinghe, C. S., Marino, D., Yücel, F., Bulut, E., & Manic, M. (2019) · Conference on Human System Interactions · doi:10.1109/hsi47298.2019.8942633 · Cited by 5
SiamVGG-LLC: Visual Tracking Using LLC and Deeper Siamese Networks
Yin, Z., Wen, C., Huang, Z., et al. (2019) · IEEE International Conference on Communication Technology (ICCT) · doi:10.1109/icct46805.2019.8947122 · Cited by 5
Rolling in the Deep Convolutional Neural Networks
Soydemir, D. (2019) · International Journal of Intelligent Systems and Applications in Engineering · doi:10.18201/ijisae.2019457674 · Cited by 5
Indirect Stochastic Gradient Quantization and Its Application in Distributed Deep Learning
Abdi, A., & Fekri, F. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.5707 · Cited by 5
The expressivity and training of deep neural networks: Toward the edge of chaos?
Zhang, G., Li, G., Shen, W., & Zhang, W. (2019) · Neurocomputing · doi:10.1016/j.neucom.2019.12.044 · Cited by 6
Mabu, S., Atsumo, A., Kido, S., Kuremoto, T., & Hirano, Y. (2020) · Journal of Signal Processing Systems · doi:10.1007/s11265-019-01499-w · Cited by 5
Gambo, F. L., Wajiga, G. M., & Garba, E. J. (2019) · International Conference of the IEEE Nigeria Computer Chapter (NigeriaComputConf) · doi:10.1109/nigeriacomputconf45974.2019.8949656 · Cited by 5
A new corpus-based convolutional neural network for big data text analytics
Nahili, W., Rezega, K., & Kazar, O. (2019) · Journal of Intelligence Studies in Business · doi:10.37380/jisib.v9i2.469 · Cited by 5
Künstliche neuronale Netzwerke als Black Box
Sudmann, A. (2020) · Ars digitalis · doi:10.1007/978-3-658-27852-6_10 · Cited by 8
Training Progressively Binarizing Deep Networks using FPGAs
Lammie, C., Xiang, W., & Azghadi, M. R. (2020) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas45731.2020.9181099 · Cited by 5
Rectified Softmax Loss With All-Sided Cost Sensitivity for Age Estimation
Li, D., Ma, X., Ren, Y., et al. (2020) · IEEE Access · doi:10.1109/access.2020.2964281 · Cited by 5
Manabe, T., Tomonaga, K., & Shibata, Y. (2019) · International Symposium on Computing and Networking · doi:10.1109/candar.2019.00029 · Cited by 5
Spatial Hierarchical Analysis Deep Neural Network for RGB-D Object Recognition
Shah, S. A. A. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-39770-8_15 · Cited by 5
Age Detection from Brain MRI Images Using the Deep Learning
Siar, M., & Teshnehlab, M. (2019) · International Conference on Computer and Knowledge Engineering · doi:10.1109/iccke48569.2019.8964911 · Cited by 7
A revisit to MacKay algorithm and its application to deep network compression
Li, C., Mao, Y., Zhang, R., & Huai, J. (2020) · Frontiers of Computer Science · doi:10.1007/s11704-019-8390-z · Cited by 5
Deep Learning Methods for Image Segmentation Containing Translucent Overlapped Objects
Mahyari, T. L., & Dansereau, R. M. (2019) · IEEE Global Conference on Signal and Information Processing · doi:10.1109/globalsip45357.2019.8969558 · Cited by 5
Synthesizing Data Using Variational Autoencoders for Handling Class Imbalanced Deep Learning
Sheikh, T. S., Khan, A., Fahim, M., & Ahmad, M. I. (2020) · Communications in computer and information science · doi:10.1007/978-3-030-39575-9_28 · Cited by 5
Fast Convolutional Neural Network for Real-Time Robotic Grasp Detection
Ribeiro, E. G., & Grassi, V. (2019) · International Conference on Advanced Robotics (ICAR) · doi:10.1109/icar46387.2019.8981651 · Cited by 5
A Novel Framework for Early Fire Detection Using Terrestrial and Aerial 360-Degree Images
Barmpoutis, P., & Stathaki, T. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-40605-9_6 · Cited by 5
Nanni, L., & Brahnam, S. (2020) · Journal of Artificial Intelligence and Systems · doi:10.33969/ais.2020.21004 · Cited by 4
A Deep Learning Approach for SAR Image Fusion
Rout, M., Nahak, S., Priyadarshinee, S., Mohapatra, P., Sa, K. D., & Dash, D. (2019) · International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT) · doi:10.1109/icicict46008.2019.8993376 · Cited by 6
Crime Level Prediction using Stacked Maps with Deep Convolutional Autoencoder
Esquivel, N., Peralta, B., & Nicolis, O. (2019) · IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON) · doi:10.1109/chilecon47746.2019.8988082 · Cited by 6
Estimating Vehicle Speed on Highway Roads from Smartphone Sensors Using Deep Learning Models
Amer, N. E., El-Mahdy, A., Gomaa, W. E., & Shoukry, A. (2019) · IEEE International Conference on Tools with Artificial Intelligence (ICTAI) · doi:10.1109/ictai.2019.00138 · Cited by 5
A Gradient-Interleaved Scheduler for Energy-Efficient Backpropagation for Training Neural Networks
Unnikrishnan, N. K., & Parhi, K. K. (2020) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas45731.2020.9181242 · Cited by 5
Semi-Automatic Crowdsourcing Tool for Online Food Image Collection and Annotation
Shao, Z., Mao, R., & Zhu, F. M. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9006165 · Cited by 5
Hierarchical Transfer Convolutional Neural Networks for Image Classification
Dong, X., Wu, H.-H., Yan, Y., & Qian, L. (2019) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata47090.2019.9005707 · Cited by 7
Effective Mutation and Recombination for Evolving Convolutional Networks
Dahal, B., & Zhan, J. Z. (2020) · doi:10.1145/3378184.3378187 · Cited by 5
Pathological Image Classification of Breast Cancer Based on Residual Network and Focal Loss
Shu, W., Wang, S., Chen, Q., et al. (2019) · doi:10.1145/3374587.3374634 · Cited by 5
DeepMEL: Compiling Visual Multi-Experience Localization into a Deep Neural Network
Gridseth, M., & Barfoot, T. D. (2020) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra40945.2020.9197362 · Cited by 6
Partial tracking method based on siamese network
Li, C., Lin, S., Qiao, J., et al. (2020) · The Visual Computer · doi:10.1007/s00371-020-01825-5 · Cited by 5
Interpretable Convolutional Neural Networks Using a Rule-Based Framework for Classification
Xi, Z., & Panoutsos, G. (2020) · Studies in computational intelligence · doi:10.1007/978-3-030-38704-4_1 · Cited by 6
Convolutional Neural Network Models for Content Based X-Ray Image Classification
Arti, P., Agrawal, A., Adishesh, A., Lahari, V. M., & Niranjana Krupa, B. (2019) · IEEE International WIE Conference on Electrical and Computer Engineering · doi:10.1109/wiecon-ece48653.2019.9019943 · Cited by 6
Koporec, G., & Perš, J. (2019) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2019.00310 · Cited by 6
Human action recognition based on multi-mode spatial-temporal feature fusion
Wang, D., Yang, J., & Zhou, Y. (2019) · International Conference on Information Fusion (FUSION) · doi:10.23919/fusion43075.2019.9011361 · Cited by 8
Processing and Visualization of Medical Images Using Machine Learning and Virtual Reality
Cigánek, J., & Képešiová, Z. (2020) · Cybernetics & Informatics (K&I) · doi:10.1109/ki48306.2020.9039896 · Cited by 5
Nguyen, H. M., Rattani, A., & Derakhshani, R. R. (2019) · IEEE International Symposium on Technologies for Homeland Security (HST) · doi:10.1109/hst47167.2019.9032968 · Cited by 5
High Impulse Noise Intensity Removal in Natural Images Using Convolutional Neural Network
Mafi, M., Izquierdo, W., & Adjouadi, M. (2020) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc47524.2020.9031200 · Cited by 5
Chaudhary, N., & Savari, S. A. (2020) · IEEE Transactions on Semiconductor Manufacturing · doi:10.1109/tsm.2020.2979680 · Cited by 5
An interpretable gait recognition method based on time series features
Shi, M., & Wang, Z. (2020) · Scientia Sinica Informationis · doi:10.1360/n112018-00326 · Cited by 5
Semantic Segmentation of Printed Text from Marathi Document Images using Deep Learning Methods
Akhter, S. S. M. N., & Rege, P. P. (2019) · IEEE India Council International Conference (INDICON) · doi:10.1109/indicon47234.2019.9030360 · Cited by 5
A Novel Deep Learning Based Approach for Breast Cancer Detection
Aaqib, M., Tufail, M., & Anwar, S. (2019) · International Conference on Mathematics, Actuarial Science, Computer Science and Statistics (MACS) · doi:10.1109/macs48846.2019.9024761 · Cited by 5
Wang, Y., Jin, Z., Tokuda, Y., Naoi, Y., Tomiyama, N., & Suzuki, K. (2019) · International Conference on Computing and Pattern Recognition (ICCPR) · doi:10.1145/3373509.3373566 · Cited by 6
A Deep Multi-view Learning method for Rice Grading
Chen, Y., Wu, Y., Cheng, J. S., et al. (2019) · IEEE International Conference on Real-time Computing and Robotics (RCAR) · doi:10.1109/rcar47638.2019.9044007 · Cited by 5
An end-to-end dehazing network with transitional convolution layer
Huang, S., Li, H., Yang, Y., et al. (2020) · Multidimensional Systems and Signal Processing · doi:10.1007/s11045-020-00723-2 · Cited by 5
Feature encoding with hybrid heterogeneous structure model for image classification
Ji, Z., Yang, Y., Wang, F., Xu, L., & Hu, X. (2020) · IET Image Processing · doi:10.1049/iet-ipr.2019.0719 · Cited by 5
Padilha, R., Andaló, F., & Rocha, A. D. R. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9054120 · Cited by 5
Comparative Evaluations of Human Behavior Recognition Using Deep Learning
Jia, L., & Yan, W. Q. (2020) · Advances in information security, privacy, and ethics book series · doi:10.4018/978-1-7998-2701-6.ch009 · Cited by 5
Correcting Automatic Cataract Diagnosis Systems Against Noisy/Blur Environment
Pratap, T., & Kokil, P. (2020) · National Conference on Communications · doi:10.1109/ncc48643.2020.9055998 · Cited by 5
A Comparative Study of Different CNN Encoders for Monocular Depth Prediction
Aladem, M., Chennupati, S., El-Shair, Z. A., & Rawashdeh, S. A. (2019) · IEEE National Aerospace and Electronics Conference (NAECON) · doi:10.1109/naecon46414.2019.9057857 · Cited by 6
A Study on the Composition of Image-Based Ship-type/class Identification System
Lee, J., Nam, D.-W., Lee, J., et al. (2020) · International Conference on Advanced Communication Technology · doi:10.23919/icact48636.2020.9061395 · Cited by 5
CNNs for Fine-Grained Car Model Classification
Corrales, H., Llorca, D. F., Parra, I., Vigre, S., Quintanar, A., Lorenzo, J., & Hernández, N. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-45096-0_13 · Cited by 6
Convolution Neural Networks for Arabic Font Recognition
Sakr, G. E., Mhanna, A., & Demerjian, R. (2019) · International Conference on Signal-Image Technology & Internet-Based Systems (SITIS) · doi:10.1109/sitis.2019.00031 · Cited by 5
Limonova, E. E., Control", C. S. A., Нейман-заде, М. И., "MCST", J., & Arlazarov, V. L. (2020) · Bulletin of the South Ural State University Series Mathematical Modelling Programming and Computer Software · doi:10.14529/mmp200109 · Cited by 5
Guaranteed Convergence of Training Convolutional Neural Networks via Accelerated Gradient Descent
Zhang, S., Wang, M., Liu, S., Chen, P., & Xiong, J. (2020) · Conference on Information Sciences and Systems · doi:10.1109/ciss48834.2020.1570627111 · Cited by 5
An Automatic Defect Detection Method for Gas Insulated Switchgear
Zhang, X., & Qian, Y. (2020) · IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) · doi:10.1109/itnec48623.2020.9085006 · Cited by 5
Zhang, Y., Yang, Q. Z., Zeng, Y., et al. (2020) · Microwave and Optical Technology Letters · doi:10.1002/mop.32411 · Cited by 5
DiaNet: An Efficient Multi-Grained Re-configurable Neural Network in Silicon
Zhang, R., Chen, Y., Nakada, T., & Nakashima, Y. (2019) · IEEE International System-on-Chip Conference (SOCC) · doi:10.1109/socc46988.2019.1570548015 · Cited by 5
Convolutional Neural Network For Predicting The Spread of Cancer
Lantang, O. A., Tiba, A., Hajdú, A., et al. (2019) · IEEE International Conference on Cognitive Infocommunications · doi:10.1109/coginfocom47531.2019.9089939 · Cited by 5
Weight Compression MAC Accelerator for Effective Inference of Deep Learning
Maki, A., Miyashita, D., SASAKI, S., Nakata, K., Tachibana, F., Suzuki, T., Deguchi, J., & FUJIMOTO, R. (2020) · IEICE Transactions on Electronics · doi:10.1587/transele.2019ctp0007 · Cited by 5
Multi spectral classification and recognition of breast cancer and pneumonia
Kakde, A., Arora, N., Sharma, D., et al. (2020) · Polish Journal of Medical Physics And Engineering · doi:10.2478/pjmpe-2020-0001 · Cited by 5
Research on Facial Expression Recognition Based on Deep Learning
Jin, X., & Xu, Y. (2019) · International Conference on Electronic Information Technology and Computer Engineering (EITCE) · doi:10.1109/eitce47263.2019.9095140 · Cited by 5
TQVS: Temporal Queries over Video Streams in Action
Chen, Y., Yu, X., & Koudas, N. (2020) · ACM SIGMOD International Conference on Management of Data (SIGMOD) · doi:10.1145/3318464.3384693 · Cited by 5
Deep Supervised Hashing with Dynamic Weighting Scheme
Sun, Y., & Yu, S. (2020) · IEEE International Conference on Big Data Analytics (ICBDA) · doi:10.1109/icbda49040.2020.9101274 · Cited by 5
Detection of Semantic Risk Situations in Lifelog Data for Improving Life of Frail People
Yebda, T., Benois‐Pineau, J., Pech, M., et al. (2020) · International Conference on Multimedia Retrieval · doi:10.1145/3372278.3391931 · Cited by 5
An Adaptive Multiscale Fusion Network Based on Regional Attention for Remote Sensing Images
Lu, W., Liang, L., Wu, X., et al. (2020) · IEEE Access · doi:10.1109/access.2020.3000425 · Cited by 5
Aslam, A. (2020) · International Conference on Multimedia Retrieval · doi:10.1145/3372278.3391936 · Cited by 5
Class-dependent Pruning of Deep Neural Networks
Entezari, R., & Saukh, O. (2020) · IEEE Workshop on Machine Learning on Edge in Sensor Systems (SenSys-ML) · doi:10.1109/sensysml50931.2020.00010 · Cited by 5
CNC Machine Tool Wear Monitoring Based on Densely Connected Convolutional Networks
Song, H., Gao, H., Guo, L., et al. (2020) · Prognostics and Health Management Conference (PHM) · doi:10.1109/phm-besancon49106.2020.00013 · Cited by 5
Deep Convolutional Likelihood Particle Filter for Visual Tracking
Mozhdehi, R. J., & Medeiros, H. P. (2021) · Transactions on computational science and computational intelligence · doi:10.1007/978-3-030-71051-4_2 · Cited by 5
A Deep Learning Framework for Robust Semantic SLAM
Azzam, R., Taha, T., Huang, S., & Zweiri, Y. (2020) · Advances in Science and Engineering Technology International Conferences (ASET) · doi:10.1109/aset48392.2020.9118181 · Cited by 6
Sahebdivani, S., Arefi, H., & Maboudi, M. (2020) · doi:10.1109/mvip49855.2020.9116894 · Cited by 5
A ConvNet Based Procedure for Image Copy-Move Forgery Detection
Kumar, A., & Soni, B. (2020) · Communications in computer and information science · doi:10.1007/978-981-15-6315-7_26 · Cited by 6
Pie Chart or Pizza: Identifying Chart Types and Their Virality on Twitter
Vougiouklis, P., Carr, L. A., & Simperl, E. P. B. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7335 · Cited by 5
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
