The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Identity Mappings in Deep Residual Networks
Open accessOverview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
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
“By deepening the network and addressing the problem of a limited training dataset, the architecture of ResNet18 [30] improves the performance of visual categorization.”
From An efficient deepfake video detection using robust deep learning (2024) · cites this paper as [30]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Thus, the structure of ShuffleNet V2 realizes this type of feature re-use pattern by design . It shares the similar benefit of feature re-use for high accuracy as in DenseNet [6], but it is much more efficient as analyzed earlier.”
From ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design (2018) · cites this paper as [6]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Second, in each block, half of feature channels (when c ′ = c/ 2) directly go through the block and join the next block. This can be regarded as a kind of feature reuse , in a similar spirit as in DenseNet [6] and CondenseNet [16].”
From ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design (2018) · cites this paper as [6]
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Deep Residual Learning for Image Recognition
He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474
Self-citationHochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380
Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification
He, K., Zhang, X., Ren, S., & Sun, J. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.123 · Cited by 19,741
Self-citationBackpropagation Applied to Handwritten Zip Code Recognition
LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636
Research works printed but not linked: 16
- Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: ICML (2010)
- Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part V. LNCS, vol. 8693, pp. 740–755. Springer, Heidelberg (2014) · DOI · Paper page
- Srivastava, R.K., Greff, K., Schmidhuber, J.: Highway networks. In: ICML Workshop (2015)
- Srivastava, R.K., Greff, K., Schmidhuber, J.: Training very deep networks. In: NIPS (2015)
- Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: ICML (2015)
- Hinton, G.E., Srivastava, N., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.R.: Improving neural networks by preventing co-adaptation of feature detectors (2012). arXiv:1207.0580 · arXiv
- Clevert, D.A., Unterthiner, T., Hochreiter, S.: Fast and accurate deep network learning by exponential linear units (ELUs). In: ICLR (2016)
- Graham, B.: Fractional max-pooling (2014). arXiv:1412.6071 · arXiv
- Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: Striving for simplicity: the all convolutional net (2014). arXiv:1412.6806 · arXiv
- Lin, M., Chen, Q., Yan, S.: Network in network. In: ICLR (2014)
- Lee, C.Y., Xie, S., Gallagher, P., Zhang, Z., Tu, Z.: Deeply-supervised nets. In: AISTATS (2015)
- Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: Fitnets: hints for thin deep nets. In: ICLR (2015)
- Mishkin, D., Matas, J.: All you need is a good init. In: ICLR (2016)
- Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: CVPR (2016) · DOI · Paper page
- Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: Going deeper with convolutions. In: CVPR (2015) · DOI · Paper page
- Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: ICLR (2015)
Cited by
Sorted by citations
Items 101–188 of 188
Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning
Chen, T., Liu, S., Chang, S., Cheng, Y., Amini, L., & Wang, Z. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00078 · Cited by 205
How Do Vision Transformers Work?
Park, N., & Kim, S. (2022) · arXiv · doi:10.48550/arxiv.2202.06709 · Cited by 356
“Figure 11 shows AlterNet based on pre-activation ResNet-50 (He et al., 2016b) for CIFAR-100 as an example.”
MethodStabilizing Transformers for Reinforcement Learning
Parisotto, E., Song, H. F., Rae, J. W., Pascanu, R., Gulcehre, C., Jayakumar, S. M., Jaderberg, M., Kaufman, R. L., Clark, A., Noury, S., et al. (2019) · arXiv · doi:10.48550/arxiv.1910.06764 · Cited by 199
Dual-force convolutional neural networks for accurate brain tumor segmentation
Chen, S., Ding, C., & Liu, M. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2018.11.009 · Cited by 195
Leichtmann, B., Humer, C., Hinterreiter, A., Streit, M., & Mara, M. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107539 · Cited by 200
CMU DeepLens: deep learning for automatic image-based galaxy–galaxy strong lens finding
Lanusse, F., Ma, Q., Li, N., Collett, T. E., Li, C.-L., Ravanbakhsh, S., Mandelbaum, R., & Póczos, B. (2018) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/stx1665 · Cited by 191
SPOT-Disorder2: Improved Protein Intrinsic Disorder Prediction by Ensembled Deep Learning
Hanson, J., Paliwal, K. K., Litfin, T., & Zhou, Y. (2019) · Genomics, Proteomics & Bioinformatics · doi:10.1016/j.gpb.2019.01.004 · Cited by 165
Active Convolution: Learning the Shape of Convolution for Image Classification
Jeon, Y., & Kim, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.200 · Cited by 180
Non-iterative and Fast Deep Learning: Multilayer Extreme Learning Machines
Zhang, J., Li, Y., Xiao, W., & Zhang, Z. (2020) · Journal of the Franklin Institute · doi:10.1016/j.jfranklin.2020.04.033 · Cited by 179
Glow: Generative Flow with Invertible 1x1 Convolutions
Kingma, D. P., & Dhariwal, P. (2018) · arXiv · Cited by 825
Multiple wavelet coefficients fusion in deep residual networks for fault diagnosis
Zhao, M., Kang, M., Tang, B., & Pecht, M. (2019) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2018.2866050 · Cited by 174
ResNet or DenseNet? Introducing Dense Shortcuts to ResNet
Zhang, C., Benz, P., Argaw, D. M., Lee, S., Kim, J., Rameau, F., Bazin, J.-C., & Kweon, I. S. (2021) · 2021 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv48630.2021.00359 · Cited by 174
Jung, J.-W., Heo, H.-S., Kim, J.-H., Shim, H.-J., & Yu, H.-J. (2019) · Interspeech 2019 · doi:10.21437/interspeech.2019-1982 · Cited by 166
Self-supervised learning for human activity recognition using 700,000 person-days of wearable data
Yuan, H., Chan, S., Creagh, A. P., Tong, C., Acquah, A., Clifton, D. A., & Doherty, A. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01062-3 · Cited by 166
“We adapted a ResNet-V2 with 18 layers and 1D convolutions (He et al., 2016) for the main trunk (feature extractor), totalling 10M parameters.”
Efficient densely connected convolutional neural networks
Li, G., Zhang, M., Li, J., Lv, F., & Tong, G. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2020.107610 · Cited by 145
DeepHeart: Semi-Supervised Sequence Learning for Cardiovascular Risk Prediction
Ballinger, B., Hsieh, J., Singh, A., Sohoni, N., Wang, J., Tison, G., Marcus, G., Sanchez, J., Maguire, C., Olgin, J., et al. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11891 · Cited by 156
Crafting GBD-Net for Object Detection
Zeng, X., Ouyang, W., Yan, J., Li, H., Xiao, T., Wang, K., Liu, Y., Zhou, Y., Yang, B., Wang, Z., et al. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2745563 · Cited by 142
Multi-modal learning from unpaired images: application to multi-organ segmentation in CT and MRI
Valindria, V. V., Pawlowski, N., Rajchl, M., Lavdas, I., Aboagye, E. O., Rockall, A. G., Rueckert, D., & Glocker, B. (2018) · 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) · doi:10.1109/wacv.2018.00066 · Cited by 146
ResNet-Based Vehicle Classification and Localization in Traffic Surveillance Systems
Jung, H., Choi, M.-K., Jung, J., Lee, J.-H., Kwon, S., & Jung, W. Y. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.129 · Cited by 137
Human activity recognition using wearable sensors by heterogeneous convolutional neural networks
Han, C., Zhang, L., Tang, Y., Huang, W., Min, F., & He, J. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.116764 · Cited by 130
Accelerating Deep Convolutional Networks using low-precision and sparsity
Venkatesh, G., Nurvitadhi, E., & Marr, D. (2017) · 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2017.7952679 · Cited by 123
Real-time Deep Pose Estimation with Geodesic Loss for Image-to-Template Rigid Registration
Salehi, S. S. M., Khan, S., Erdogmus, D., & Gholipour, A. (2018) · arXiv · doi:10.48550/arxiv.1803.05982 · Cited by 118
A deep 3D residual CNN for false‐positive reduction in pulmonary nodule detection
Jin, H., Li, Z., Tong, R., & Lin, L. (2018) · Medical Physics · doi:10.1002/mp.12846 · Cited by 118
Pixel-Based Image Encryption Without Key Management for Privacy-Preserving Deep Neural Networks
Sirichotedumrong, W., Kinoshita, Y., & Kiya, H. (2019) · IEEE Access · doi:10.1109/access.2019.2959017 · Cited by 105
Jung, J.-W., Kim, S.-B., Shim, H.-J., Kim, J.-H., & Yu, H.-J. (2020) · Interspeech 2020 · doi:10.21437/interspeech.2020-1011 · Cited by 109
Fault diagnosis of high-speed train bogie by residual-squeeze net
Su, L., Ma, L., Qin, N., Huang, D., & Kemp, A. H. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2907373 · Cited by 102
Namburete, A. I. L., Xie, W., Yaqub, M., Zisserman, A., & Noble, J. A. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.02.006 · Cited by 95
Passive acoustic monitoring of animal populations with transfer learning
Dufourq, E., Batist, C., Foquet, R., & Durbach, I. (2022) · Ecological Informatics · doi:10.1016/j.ecoinf.2022.101688 · Cited by 97
Adaptive deep learning model selection on embedded systems
Taylor, B., Marco, V. S., Wolff, W., Elkhatib, Y., & Wang, Z. (2018) · Proceedings of the 19th ACM SIGPLAN/SIGBED International Conference on Languages, Compilers, and Tools for Embedded Systems · doi:10.1145/3211332.3211336 · Cited by 90
Deep learning approaches for detection and removal of ghosting artifacts in MR spectroscopy
Kyathanahally, S. P., Döring, A., & Kreis, R. (2018) · Magnetic Resonance in Medicine · doi:10.1002/mrm.27096 · Cited by 92
DFDT: An End-to-End DeepFake Detection Framework Using Vision Transformer
Khormali, A., & Yuan, J.-S. (2022) · Applied Sciences · doi:10.3390/app12062953 · Cited by 88
Mariani, S., Rendu, Q., Urbani, M., & Sbarufatti, C. (2021) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2021.107748 · Cited by 86
Deep convolution stack for waveform in underwater acoustic target recognition
Tian, S., Chen, D., Wang, H., & Liu, J. (2021) · Scientific Reports · doi:10.1038/s41598-021-88799-z · Cited by 86
A Compact Convolutional Neural Network for Surface Defect Inspection
Huang, Y., Qiu, C., Wang, X., Wang, S., & Yuan, K. (2020) · Sensors · doi:10.3390/s20071974 · Cited by 85
A Capsule Network Based Approach for Detection of Audio Spoofing Attacks
Luo, A., Li, E., Liu, Y., Kang, X., & Wang, Z. J. (2021) · ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp39728.2021.9414670 · Cited by 84
Adversarial Noise Layer: Regularize Neural Network by Adding Noise
You, Z., Ye, J., Li, K., Xu, Z., & Wang, P. (2019) · 2019 IEEE International Conference on Image Processing (ICIP) · doi:10.1109/icip.2019.8803055 · Cited by 80
An efficient deepfake video detection using robust deep learning
Qadir, A., Mahum, R., El-Meligy, M. A., Ragab, A. E., AlSalman, A., & Awais, M. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e25757 · Cited by 78
Subject-Aware Contrastive Learning for Biosignals
Cheng, J. Y., Goh, H., Dogrusoz, K., Tuzel, O., & Azemi, E. (2020) · arXiv · doi:10.48550/arxiv.2007.04871 · Cited by 74
Miller, J., Taori, R., Raghunathan, A., Sagawa, S., Koh, P. W., Shankar, V., Liang, P., Carmon, Y., & Schmidt, L. (2021) · arXiv · doi:10.48550/arxiv.2107.04649 · Cited by 74
Li, L. (2019) · Remote Sensing · doi:10.3390/rs11111378 · Cited by 70
Boosting Fast Adversarial Training With Learnable Adversarial Initialization
Jia, X., Zhang, Y., Wu, B., Wang, J., & Cao, X. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3184255 · Cited by 69
Fdftnet: facing off fake images using fake detection fine-tuning network
Jeon, H., Bang, Y., & Woo, S. S. (2020) · IFIP Advances in Information and Communication Technology · doi:10.1007/978-3-030-58201-2_28 · Cited by 65
Blockchain-Federated-Learning and Deep Learning Models for COVID-19 Detection Using CT Imaging
Kumar, R., Khan, A. A., Kumar, J., Zakria, Golilarz, N. A., Zhang, S., Ting, Y., Zheng, C., & Wang, W. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3076767 · Cited by 57
A Multimodal Knowledge-enhanced Whole-slide Pathology Foundation Model
Xu, Y., Wang, Y., Zhou, F., Ma, J., Jin, C., Yang, S., Li, J., Zhang, Z., Zhao, C., Zhou, H., et al. (2024) · arXiv · doi:10.48550/arxiv.2407.15362 · Cited by 60
Self-citationOn the Intrinsic Dimensionality of Image Representations
Gong, S., Boddeti, V. N., & Jain, A. K. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00411 · Cited by 59
Chan, S., Hang, Y., Tong, C., Acquah, A., Schonfeldt, A., Gershuny, J., & Doherty, A. (2024) · Scientific Data · doi:10.1038/s41597-024-03960-3 · Cited by 61
EasiEdge: A Novel Global Deep Neural Networks Pruning Method for Efficient Edge Computing
Yu, F., Cui, L., Wang, P., Han, C., Huang, R., & Huang, X. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2020.3034925 · Cited by 58
Model metamers reveal divergent invariances between biological and artificial neural networks
Feather, J., Leclerc, G., Mądry, A., & McDermott, J. H. (2023) · Nature Neuroscience · doi:10.1038/s41593-023-01442-0 · Cited by 59
Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging
Meng, Q., Housden, J., Matthew, J., Rueckert, D., Schnabel, J. A., Kainz, B., Sinclair, M., Zimmer, V., Hou, B., Rajchl, M., et al. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2913311 · Cited by 52
ADD: Attention-Based DeepFake Detection Approach
Khormali, A., & Yuan, J.-S. (2021) · Big Data and Cognitive Computing · doi:10.3390/bdcc5040049 · Cited by 55
Expression Recognition Method Based on a Lightweight Convolutional Neural Network
Zhao, G., Yang, H., & Yu, M. (2020) · IEEE Access · doi:10.1109/access.2020.2964752 · Cited by 53
Bilal, M., Maqsood, M., Yasmin, S., Hasan, N. U., & Rho, S. (2022) · The Journal of Supercomputing · doi:10.1007/s11227-021-03957-4 · Cited by 51
Learning From Synthetic Photorealistic Raindrop for Single Image Raindrop Removal
Hao, Z., You, S., Li, Y., Li, K., & Lu, F. (2019) · 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) · doi:10.1109/iccvw.2019.00534 · Cited by 54
Yang, P., Yin, X., Lu, H., Hu, Z., Zhang, X., Jiang, R., & Lv, H. (2022) · Medical Image Analysis · doi:10.1016/j.media.2022.102539 · Cited by 50
Self-citationFusing multi-scale information in convolution network for MR image super-resolution reconstruction
Liu, C., Wu, X., Yu, X., Tang, Y., Zhang, J., & Zhou, J. (2018) · BioMedical Engineering OnLine · doi:10.1186/s12938-018-0546-9 · Cited by 47
Regularizing deep networks with prior knowledge: A constraint-based approach
Roychowdhury, S., Diligenti, M., & Gori, M. (2021) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.106989 · Cited by 46
Training Object Detectors With Noisy Data
Chadwick, S., & Newman, P. (2019) · 2019 IEEE Intelligent Vehicles Symposium (IV) · doi:10.1109/ivs.2019.8814137 · Cited by 46
Efficient Contextualized Representation: Language Model Pruning for Sequence Labeling
Liu, L., Ren, X., Shang, J., Gu, X., Peng, J., & Han, J. (2018) · Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d18-1153 · Cited by 45
Knowledge from the Original Network: Restore a Better Pruned Network with Knowledge Distillation
Chen, L., Chen, Y., Xi, J., & Le, X. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-020-00248-y · Cited by 48
Yuan, H., Plekhanova, T., Walmsley, R., Reynolds, A. C., Maddison, K. J., Bucan, M., Gehrman, P., Rowlands, A., Ray, D. W., Bennett, D., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01065-0 · Cited by 40
Generating Healthcare Time Series Data for Improving Diagnostic Accuracy of Deep Neural Networks
Maweu, B. M., Shamsuddin, R., Dakshit, S., & Prabhakaran, B. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3077049 · Cited by 35
Ge, W., Patino, J., Todisco, M., & Evans, N. (2022) · ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp43922.2022.9747476 · Cited by 40
Zhang, F., Yang, F., Li, C., & Yuan, G. (2019) · IEEE Access · doi:10.1109/access.2019.2919103 · Cited by 29
“Since residual networks [14], [38] have achieved great success, recently, various deep fusion residual network architectures have been designed to enhance the representation power”
BackgroundDeep-recursive residual network for image semantic segmentation
Zhang, Y., Li, X., Lin, M., Chiu, B., & Zhao, M. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-04738-5 · Cited by 27
Untangling Dense Non-Planar Knots by Learning Manipulation Features and Recovery Policies
Sundaresan*, P., Grannen*, J., Thananjeyan, B., Balakrishna, A., Ichnowski, J., Novoseller, E., Hwang, M., Laskey, M., Gonzalez, J., & Goldberg, K. (2021) · Robotics: Science and Systems XVII · doi:10.15607/rss.2021.xvii.013 · Cited by 26
Object Localization Based on Proposal Fusion
Tang, S., Li, Y., Deng, L., & Zhang, Y. (2017) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2017.2729786 · Cited by 25
Park, J., Kwak, I.-Y., & Lim, C. (2021) · Electronics · doi:10.3390/electronics10161996 · Cited by 24
Joint Age Estimation and Gender Classification of Asian Faces Using Wide ResNet
Huynh, H. T., & Nguyen, H. (2020) · SN Computer Science · doi:10.1007/s42979-020-00294-w · Cited by 20
Small, S. R., Chan, S., Walmsley, R., Fritsch, L. V., Acquah, A., Mertes, G., Feakins, B. G., Creagh, A., Strange, A., Matthews, C. E., et al. (2023) · doi:10.1101/2023.02.20.23285750 · Cited by 22
Zhang, C., Yu, Z., Wang, X., Chen, Z.-J., Deng, C., & Xie, S. Q. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106551 · Cited by 21
MaxDropout: Deep Neural Network Regularization Based on Maximum Output Values
do Santos, C. F. G., Colombo, D., Roder, M., & Papa, J. P. (2021) · 2020 25th International Conference on Pattern Recognition (ICPR) · doi:10.1109/icpr48806.2021.9412733 · Cited by 20
Automatic annotation of cervical vertebrae in videofluoroscopy images via deep learning
Zhang, Z., Mao, S., Coyle, J., & Sejdić, E. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102218 · Cited by 18
Weimann, K., & Conrad, T. O. F. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3427787 · Cited by 19
A Bayesian Deep Learning Approach to Near‐Term Climate Prediction
Luo, X., Nadiga, B. T., Park, J. H., Ren, Y., Xu, W., & Yoo, S. (2022) · Journal of Advances in Modeling Earth Systems · doi:10.1029/2022ms003058 · Cited by 18
RWKV: Reinventing RNNs for the Transformer Era
Peng, B., Alcaide, E., Anthony, Q., Albalak, A., Arcadinho, S., Biderman, S., Cao, H., Cheng, X., Chung, M., Grella, M., et al. (2023) · arXiv · doi:10.48550/arxiv.2305.13048 · Cited by 17
“Custom Initialization Building on principles from previous works (He et al., 2016; Jumper et al., 2021), we initialize parameters to values as similar as possible to an identity mapping while breaking symmetry so there is a clean information path.”
MethodWeakly Supervised Learning of Placental Ultrasound Images with Residual Networks
Qi, H., Collins, S., & Noble, A. (2017) · Communications in computer and information science · doi:10.1007/978-3-319-60964-5_9 · Cited by 17
Wu, B., Ross W. K., P., Ludivig, P., Chung, A. S., & Seabrook, T. (2019) · 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros40897.2019.8968124 · Cited by 15
Mitigating Adversarial Attacks in Federated Learning with Trusted Execution Environments
Queyrut, S., Schiavoni, V., & Felber, P. (2023) · 2023 IEEE 43rd International Conference on Distributed Computing Systems (ICDCS) · doi:10.1109/icdcs57875.2023.00069 · Cited by 18
A Label Efficient Semi Self-Supervised Learning Framework for IoT Devices in Industrial Process
Bharti, V., Kumar, A., Purohit, V., Singh, R., Singh, A. K., & Singh, S. K. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3289184 · Cited by 15
Thinking Fast and Slow: Efficient Text-to-Visual Retrieval with Transformers
Miech, A., Alayrac, J.-B., Laptev, I., Sivic, J., & Zisserman, A. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00970 · Cited by 149
Federated Learning with Dynamic Transformer for Text to Speech
Hong, Z., Wang, J., Qu, X., Liu, J., Zhao, C., & Xiao, J. (2021) · Interspeech 2021 · doi:10.21437/interspeech.2021-2039 · Cited by 11
“Following the residual block in ResNetV2 [24] and its symmetric structure explanation, the Front-LN method puts the layer normalization inside the residual connection and equips it with an additional final-layer normalization before prediction.”
MethodInfluentialBarreto, A. G., de Oliveira, J. M., Gois, F. N. B., Cortez, P. C., & de Albuquerque, V. H. C. (2023) · Bioengineering · doi:10.3390/bioengineering10091098 · Cited by 8
A novel connectivity of deep convolutional neural networks
Shen, Z., & Liu, Y. (2017) · 2017 Chinese Automation Congress (CAC) · doi:10.1109/cac.2017.8244187 · Cited by 5
Physical-aware model accuracy estimation for protein complex using deep learning method
Wang, H., Sun, M., Xie, L., Liu, D., & Zhang, G. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.01.017 · Cited by 5
“In this study, we designed a fusion network architecture, where each part is drawn from an equivariant graph neural network (EGNN) [20] coupled with invariant point attention (IPA) [1] and a ResNet network [39] with attention mechanism.”
Rami Naidji, M., & Elberrichi, Z. (2024) · International Journal of Computing and Digital Systems · doi:10.12785/ijcds/1501102 · Cited by 4
Deepfake Detection Using Transfer Learning
Khan, S. A., & Valles, D. (2024) · 2024 IEEE 15th Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON) · doi:10.1109/uemcon62879.2024.10754706 · Cited by 2
Ye, J., Mao, Y., Song, J., Wang, X., Jin, C., & Song, M. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i3.20219 · Cited by 1
Improved regularization of convolutional neural networks with cutout
DeVries, T., & Taylor, G. W. (2017) · arXiv · doi:10.48550/arxiv.1708.04552 · Cited by 2,776
Metadata
- Authors
- He, K., Zhang, X., Ren, S., & Sun, J.
- Affiliations
- Microsoft Research Asia (China) (CN)
- Corresponding author
- He, Kaiming
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 630-645
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2302255633
- Cited by
- 10,741
- References
- 6
- Citations by year
- 2026: 466 · 2025: 909 · 2024: 1286 · 2023: 1317 · 2022: 1365 · 2021: 1599 · 2020: 1446 · 2019: 1178 · 2018: 732 · 2017: 366 · 2016: 72
- FWCI
- 266.27
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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 › Artificial Intelligence › Stochastic Gradient Optimization Techniques
- Keywords
- ResNet, identity mapping, skip connections, residual blocks, signal propagation, generalization, training stability
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
