The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Open accessAbstract
Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One example is the Inception architecture that has been shown to achieve very good performance at relatively low computational cost. Recently, the introduction of residual connections in conjunction with a more traditional architecture has yielded state-of-the-art performance in the 2015 ILSVRC challenge; its performance was similar to the latest generation Inception-v3 network. This raises the question: Are there any benefits to combining Inception architectures with residual connections? Here we give clear empirical evidence that training with residual connections accelerates the training of Inception networks significantly. There is also some evidence of residual Inception networks outperforming similarly expensive Inception networks without residual connections by a thin margin. We also present several new streamlined architectures for both residual and non-residual Inception networks. These variations improve the single-frame recognition performance on the ILSVRC 2012 classification task significantly. We further demonstrate how proper activation scaling stabilizes the training of very wide residual Inception networks. With an ensemble of three residual and one Inception-v4 networks, we achieve 3.08% top-5 error on the test set of the ImageNet classification (CLS) challenge.
Overview
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
“In addition, in the majority of vehicle detection systems DL models are trained using weights of pre-trained models based on large scale datasets such as MS-COCO dataset [78], which contains regular and high resolution images in their natural context.”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [78]
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 use of a pre-trained model, trained on images captured from a ground viewpoint such as MS-COCO [78] and Imagenet [57] datasets, leads to weak performance in the detection of vehicles from UAVs, due to the different characteristics of the captured images.”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [78]
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
“where multi-scale convolutional layer and convolution of length 1 enable model to extract rich high-dimensional features with limited parameters.”
From IENet: a robust convolutional neural network for EEG based brain-computer interfaces (2022) · cites this paper as [19]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“Szegedy et al. [103, 109, 110] proposed Inception-ResNet and Inception-V3/4 as upgraded types of Inception-V1/2.”
From Review of deep learning: concepts, CNN architectures, challenges, applications, future directions (2021) · cites this paper as [109]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“However, since drone images have aerial views with occlusions, varying sizes and orientations, employing DL models pre-trained on datasets such as Imagenet [57] and MS-COCO [78] datasets might not provide high performance.”
From A Systematic Review of Drone Based Road Traffic Monitoring System (2022) · cites this paper as [78]
Experimental — accuracy not yet measured
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“By applying convolutions of different lengths in the convolutional layer, IENet is capable of generating the long-term and short-term features of EEG simultaneously to improve feature diversity [19, 62].”
From IENet: a robust convolutional neural network for EEG based brain-computer interfaces (2022) · cites this paper as [19]
Experimental — accuracy not yet measuredThe check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“Second, IGCV2 and IGCV3 are slow. This is due to usage of too many convolution groups (4 or 8 in [27,28]).”
From ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design (2018) · cites this paper as [27]
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
1 retracted
RETRACTED CHAPTER: Large-Scale Video Classification with Convolutional Neural Networks
Pranati, B. S., Suma, D., Latha, C. M., & Putheti, S. (2020) · Smart innovation, systems and technologies · doi:10.1007/978-981-15-7062-9_69 · Cited by 89
RetractedDeep 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
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
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
Self-citationImageNet 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
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684
Rethinking the Inception Architecture for Computer Vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.308 · Cited by 32,138
Self-citationBatch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S., & Szegedy, C. (2015) · arXiv · doi:10.48550/arxiv.1502.03167 · Cited by 27,818
Self-citation
Cited by
Sorted by citations
Items 501–600 of 1,126
Deep Learning-Based Transfer Learning for Classification of Skin Cancer
Jain, S., Singhania, U., Tripathy, B., Nasr, E. A., Aboudaif, M. K., & Kamrani, A. K. (2021) · Sensors · doi:10.3390/s21238142 · Cited by 169
Deep Specialized Network for Illuminant Estimation
Shi, W., Loy, C. C., & Tang, X. (2016) · Lecture notes in computer science · doi:10.1007/978-3-319-46493-0_23 · Cited by 169
Li, A., Sun, J., Li, P., Pu, Y., Li, H. H., & Chen, Y. (2021) · ACM Annual International Conference on Mobile Computing and Networking (MobiCom) · doi:10.1145/3447993.3483278 · Cited by 177
Highway and Residual Networks learn Unrolled Iterative EstimationPreprint
Greff, K., Srivastava, R. K., & Schmidhuber, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.07771 · Cited by 203
PKU-MMD: A Large Scale Benchmark for Continuous Multi-Modal Human Action UnderstandingPreprint
Liu, C., Hu, Y., Li, Y., Song, S., & Liu, J. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.07475 · Cited by 169
COVID-19 image classification using deep learning: Advances, challenges and opportunities
Aggarwal, P., Mishra, N. K., Fatimah, B., Singh, P., Gupta, A., & Joshi, S. D. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105350 · Cited by 168
A review on computer vision systems in monitoring of poultry: A welfare perspective
Okinda, C., Nyalala, I., Korohou, T., Okinda, C., Wang, J., Achieng, T., Wamalwa, P., Mang, T., & Shen, M. (2020) · Artificial Intelligence in Agriculture · doi:10.1016/j.aiia.2020.09.002 · Cited by 179
Improving CNN-RNN Hybrid Networks for Handwriting Recognition
Dutta, K., Krishnan, P., Mathew, M., & Jawahar, C. V. (2018) · International Conference on Frontiers in Handwriting Recognition · doi:10.1109/icfhr-2018.2018.00023 · Cited by 167
Deep Neural Network Approximation for Custom Hardware
Wang, E., Davis, J. J., Zhao, R., Ng, H.-C., Niu, X., Luk, W. W., Cheung, P. Y. K., & Constantinides, G. A. (2019) · ACM Computing Surveys · doi:10.1145/3309551 · Cited by 175
Deep learning-based personality recognition from text posts of online social networks
Xue, D., Wu, L., Zheng, H., Guo, S., Gao, L., Wu, Z., Zhong, X., & Sun, J. (2018) · Applied Intelligence · doi:10.1007/s10489-018-1212-4 · Cited by 169
RENAS: Reinforced Evolutionary Neural Architecture Search
Chen, Y., Meng, G., Zhang, Q., Xiang, S., Huang, C., Mu, L., & Wang, X. (2019) · 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.2019.00492 · Cited by 175
Saha, A., Hosseinzadeh, M., & Huisman, H. J. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102155 · Cited by 175
An Automated Convolutional Neural Network Based Approach for Paddy Leaf Disease Detection
Islam, M. A., Rahman, M. N., Shamsojjaman, M., Hasan, S. M. N., Shahadat, M., & Khatun, T. (2021) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2021.0120134 · Cited by 165
Loddo, A., Buttau, S., & Ruberto, C. D. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.105032 · Cited by 169
Surface Electromyography as a Natural Human–Machine Interface: A Review
Zheng, M., Crouch, M. S., & Eggleston, M. S. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3165988 · Cited by 171
Deep Learning Methods for Heart Sounds Classification: A Systematic Review
Chen, W., Sun, Q., Chen, X., Xie, G., Wu, H., & Xu, C. (2021) · Entropy · doi:10.3390/e23060667 · Cited by 172
A survey on Deep Learning Advances on Different 3D Data RepresentationsPreprint
Ahmed, E., Saint, A., Shabayek, A. E. R., Cherenkova, K., Das, R., Gusev, G., Aouada, D., & Ottersten, B. (2018) · arXiv (Cornell University) · doi:10.13140/rg.2.2.32083.02080 · Cited by 194
Learning supervised scoring ensemble for emotion recognition in the wild
Hu, P., Cai, D., Wang, S., Yao, A., & Chen, Y. (2017) · Proceedings of the 19th ACM International Conference on Multimodal Interaction · doi:10.1145/3136755.3143009 · Cited by 162
Grid Path Planning with Deep Reinforcement Learning: Preliminary Results
Panov, A. I., Yakovlev, K., & Suvorov, R. E. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.01.054 · Cited by 168
Exploring Disentangled Feature Representation Beyond Face Identification
Liu, Y., Wei, F., Shao, J., Sheng, L., Yan, J., & Wang, X. (2018) · 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.2018.00222 · Cited by 161
MDFN: Multi-scale deep feature learning network for object detection
Ma, W., Wu, Y., Cen, F., & Wang, G. (2019) · Pattern Recognition · doi:10.1016/j.patcog.2019.107149 · Cited by 163
Tang, X., Li, W., Li, X., Ma, W., & Dang, X. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113285 · Cited by 163
Cha, D., Pae, C., Seong, S.-B., Choi, J. Y., & Park, H. (2019) · EBioMedicine · doi:10.1016/j.ebiom.2019.06.050 · Cited by 160
Structured Binary Neural Networks for Accurate Image Classification and Semantic Segmentation
Zhuang, B., Shen, C., Tan, M., Liu, L., & Reid, I. D. (2019) · 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.2019.00050 · Cited by 164
Universal adversarial attacks on deep neural networks for medical image classification
Hirano, H., Minagi, A., & Takemoto, K. (2021) · BMC Medical Imaging · doi:10.1186/s12880-020-00530-y · Cited by 162
Karumuri, S., Tripathy, R. K., Bilionis, I., & Panchal, J. H. (2019) · Journal of Computational Physics · doi:10.1016/j.jcp.2019.109120 · Cited by 159
Patch-Wise Attack for Fooling Deep Neural Network
Gao, L., Zhang, Q., Song, J., Liu, X., & Shen, H. T. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58604-1_19 · Cited by 168
Deep Model-Based 6D Pose Refinement in RGB
Manhardt, F., Kehl, W., Navab, N., & Tombari, F. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01264-9_49 · Cited by 158
Fixing a Broken ELBOPreprint
Alemi, A. A., Poole, B., Fischer, I., Dillon, J. V., Saurous, R. A., & Murphy, K. P. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.00464 · Cited by 195
Self-citationA Convolutional Neural Network Framework for Accurate Skin Cancer Detection
Thurnhofer-Hemsi, K., & Domínguez, E. (2021) · Neural Processing Letters · doi:10.1007/s11063-020-10364-y · Cited by 157
Using a stacked residual LSTM model for sentiment intensity prediction
Wang, J., Peng, B., & Zhang, X. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.09.049 · Cited by 158
ImageNet pre-trained models with batch normalizationPreprint
Simon, M., Rodner, E., & Denzler, J. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.01452 · Cited by 157
Pasqualetto Cassinis, L., Fonod, R., & Gill, E. (2019) · Progress in Aerospace Sciences · doi:10.1016/j.paerosci.2019.05.008 · Cited by 156
StegNet: Mega Image Steganography Capacity with Deep Convolutional Network
Wu, P., Yang, Y., & Li, X. (2018) · Future Internet · doi:10.3390/fi10060054 · Cited by 160
A Two-Stage Convolutional Neural Networks for Lung Nodule Detection
Cao, H., Liu, H., Song, E., Ma, G., Jin, R., Xu, X., Liu, T., & Hung, C. (2020) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2019.2963720 · Cited by 157
Liu, S. K., & Shi, Q. (2020) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2020.04.008 · Cited by 159
Deep Learning in Image-Based Plant Phenotyping
Murphy, K. M., Ludwig, E., Gutierrez, J., & Gehan, M. A. (2024) · Annual Review of Plant Biology · doi:10.1146/annurev-arplant-070523-042828 · Cited by 173
Convolutional neural networks for crowd behaviour analysis: a survey
Tripathi, G., Singh, K., & Vishwakarma, D. K. (2018) · The Visual Computer · doi:10.1007/s00371-018-1499-5 · Cited by 158
Identifying crop water stress using deep learning models
Chandel, N. S., Chakraborty, S. K., Rajwade, Y. A., Dubey, K., Tiwari, M. K., & Jat, D. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05325-4 · Cited by 159
RAMS-Trans: Recurrent Attention Multi-scale Transformer for Fine-grained Image Recognition
Hu, Y., Jin, X. H., Zhang⋆, Y., Hong, H., Zhang, J., He, Y., & Xue, H. (2021) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3474085.3475561 · Cited by 155
Hong, R., Liu, W., DeLair, D., Razavian, N., & Fenyö, D. (2021) · Cell Reports Medicine · doi:10.1016/j.xcrm.2021.100400 · Cited by 154
Milioto, A., Lottes, P., & Stachniss, C. (2017) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-iv-2-w3-41-2017 · Cited by 154
End-to-end malware detection for android IoT devices using deep learning
Ren, Z., Wu, H., Ning, Q., Hussain, I., & Chen, B. (2020) · Ad Hoc Networks · doi:10.1016/j.adhoc.2020.102098 · Cited by 155
A combined convolutional and recurrent neural network for enhanced glaucoma detection
Gheisari, S., Shariflou, S., Phu, J., Kennedy, P. J., Agar, A., Kalloniatis, M., & Golzan, M. (2021) · Scientific Reports · doi:10.1038/s41598-021-81554-4 · Cited by 161
MobileDets: Searching for Object Detection Architectures for Mobile Accelerators
Xiong, Y., Liu, H., Gupta, S., Akin, B., Bender, G., Wang, Y., Kindermans, P.-J., Tan, M., Singh, V., & Chen, B. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.00382 · Cited by 193
Mo, S., Zabaras, N. J., Shi, X., & Wu, J. (2020) · Water Resources Research · doi:10.1029/2019wr026082 · Cited by 157
Roslidar, R., Rahman, A., Muharar, R., Syahputra, M. R., Arnia, F., Syukri, M., Pradhan, B., & Munadi, K. (2020) · IEEE Access · doi:10.1109/access.2020.3004056 · Cited by 155
Zhang, Y., Li, H., Du, J., Qin, J., Wang, T., Chen, Y., Liu, B., Gao, W., Ma, G., & Lei, B. (2021) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2021.3062902 · Cited by 153
Deep Learning and Machine Vision Approaches for Posture Detection of Individual Pigs
Nasirahmadi, A., Sturm, B., Edwards, S. A., Jeppsson, K.-H., Olsson, A.-C., Müller, S., & Hensel, O. (2019) · Sensors · doi:10.3390/s19173738 · Cited by 150
Automatic diagnosis for thyroid nodules in ultrasound images by deep neural networks
Wang, L., Zhang, L., Zhu, M., Qi, X., & Zhang, Y. (2020) · Medical Image Analysis · doi:10.1016/j.media.2020.101665 · Cited by 151
Deep Learning for Automatic Pneumonia Detection
Gabruseva, T., Poplavskiy, D., & Kalinin, A. A. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00183 · Cited by 153
Sikkandar, M. Y., Alrasheadi, B. A., Prakash, N. B., Hemalakshmi, G. R., Mohanarathinam, A., & Shankar, K. (2020) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-020-02537-3 · Cited by 150
RetractedDMD: A Large-Scale Multi-modal Driver Monitoring Dataset for Attention and Alertness Analysis
Ortega, J. D., Kose, N., Cañas, P. N., Chao, M.-A., Unnervik, A., Nieto, M., Otaegui, O., & Salgado, L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-66823-5_23 · Cited by 165
StNet: Local and Global Spatial-Temporal Modeling for Action Recognition
He, D., Zhou, Z., Gan, C., Li, F., Liu, X., Li, Y., Wang, L., & Wen, S. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33018401 · Cited by 153
FETCH: A deep-learning based classifier for fast transient classification
Agarwal, D., Aggarwal, K., Burke-Spolaor, S., Lorimer, D. R., & Garver-Daniels, N. (2020) · Monthly Notices of the Royal Astronomical Society · doi:10.1093/mnras/staa1856 · Cited by 148
Boundary Loss for Remote Sensing Imagery Semantic Segmentation
Bokhovkin, A., & Burnaev, E. V. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-22808-8_38 · Cited by 150
Dual-branch residual network for lung nodule segmentation
Cao, H., Liu, H., Song, E., Hung, C., Ma, G., Xu, X., Jin, R., & Lü, J. (2019) · Applied Soft Computing · doi:10.1016/j.asoc.2019.105934 · Cited by 146
Cao, M.-T., Tran, Q., Nguyen, N., & Chang, K.-T. (2020) · Advanced Engineering Informatics · doi:10.1016/j.aei.2020.101182 · Cited by 150
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
Photorealistic Image Synthesis for Object Instance Detection
Hodaň, T., Vineet, V., Gal, R., Shalev, E., Hanzelka, J., Connell, T., Urbina, P., Sinha, S. N., & Guenter, B. K. (2019) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2019.8803821 · Cited by 145
Deep learning segmentation of major vessels in X-ray coronary angiography
Yang, S., Kweon, J., Roh, J., Lee, J., Kang, H., Park, L.-J., Kim, D. J., Yang, H., Hur, J., Kang, D., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-53254-7 · Cited by 145
Dhruv, P., & Naskar, S. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-981-15-1884-3_34 · Cited by 151
An adaptive data fusion strategy for fault diagnosis based on the convolutional neural network
Shi, L., Wang, H., Song, L., Wang, P., Cui, L., & Lin, T. (2020) · Measurement · doi:10.1016/j.measurement.2020.108122 · Cited by 146
Efficient and robust cell detection: A structured regression approach
Xie, Y., Xing, F., Shi, X., Kong, X., Su, H., & Yang, L. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.003 · Cited by 146
Learning Compact Recurrent Neural Networks with Block-Term Tensor Decomposition
Ye, J., Wang, L., Li, G., Chen, D., Zhe, S., Chu, X., & Xu, Z. (2018) · 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.2018.00977 · Cited by 147
BlockQNN: Efficient Block-Wise Neural Network Architecture Generation
Zhong, Z., Yang, Z., Deng, B., Yan, J., Wu, W., Shao, J., & Liu, C. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2969193 · Cited by 148
Adversarial Examples in Modern Machine Learning: A ReviewPreprint
Wiyatno, R. R., Xu, A., Dia, O., & de Berker, A. O. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.05268 · Cited by 168
Neural Network for Nanoscience Scanning Electron Microscope Image Recognition
Modarres, M. H., Aversa, R., Cozzini, S., Ciancio, R., Leto, A., & Brandino, G. P. (2017) · Scientific Reports · doi:10.1038/s41598-017-13565-z · Cited by 145
VisDrone-DET2018: The Vision Meets Drone Object Detection in Image Challenge Results
Zhu, P., Wen, L., Du, D., Bian, X., Ling, H., Hu, Q., Nie, Q., Cheng, H., Liu, C., Liu, X., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11021-5_27 · Cited by 144
Automatic Image-Based Waste Classification
Ruiz-Parrado, V., Sánchez, Á., Vélez, J. F., & Raducanu, B. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-19651-6_41 · Cited by 143
A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban Scenes
Zhang, Y., David, P., Foroosh, H., & Gong, B. (2019) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2903401 · Cited by 146
A High-Performance CNN Processor Based on FPGA for MobileNets
Wu, D., Zhang, Y., Jia, X., Tian, L., Li, T., Sui, L., Xie, D., & Shan, Y. (2019) · International Conference on Field Programmable Logic and Applications (FPL) · doi:10.1109/fpl.2019.00030 · Cited by 142
A survey of deep learning techniques for vehicle detection from UAV images
Srivastava, S., Narayan, S., & Mittal, S. (2021) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2021.102152 · Cited by 142
Deep Learning for Retail Product Recognition: Challenges and Techniques
Wei, Y.-C., Tran, S. N., Xu, S., Kang, B. H., & Springer, M. (2020) · Computational Intelligence and Neuroscience · doi:10.1155/2020/8875910 · Cited by 153
Soft Attention Improves Skin Cancer Classification Performance
Datta, S. K., Shaikh, M. A., Srihari, S. N., & Gao, M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87444-5_2 · Cited by 143
A novel ResNet101 model based on dense dilated convolution for image classification
Zhang, Q. (2021) · SN Applied Sciences · doi:10.1007/s42452-021-04897-7 · Cited by 144
He, D., Xu, K., & Zhou, P. (2019) · Computers & Industrial Engineering · doi:10.1016/j.cie.2018.12.043 · Cited by 140
An Intrusion Detection Model based on a Convolutional Neural Network
Kim, J., Shin, Y., & Choi, E. (2019) · Journal of Multimedia Information System · doi:10.33851/jmis.2019.6.4.165 · Cited by 140
Aldoj, N., Biavati, F., Michallek, F., Stober, S., & Dewey, M. (2020) · Scientific Reports · doi:10.1038/s41598-020-71080-0 · Cited by 140
Darbaghshahi, F. N., Mohammadi, M. R., & Soryani, M. (2021) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2021.3131035 · Cited by 155
Li, A., Sun, J., Zeng, X., Zhang, M., Li, H., & Chen, Y. (2021) · Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems · doi:10.1145/3485730.3485929 · Cited by 139
Fruit Detection and Segmentation for Apple Harvesting Using Visual Sensor in Orchards
Kang, H., & Chen, C. (2019) · Sensors · doi:10.3390/s19204599 · Cited by 141
Ayaz, M., Shaukat, F., & Raja, G. (2021) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-020-00966-0 · Cited by 143
Improved inception-residual convolutional neural network for object recognition
Alom, M. Z., Hasan, M., Yakopcic, C., Taha, T. M., & Asari, V. K. (2018) · Neural Computing and Applications · doi:10.1007/s00521-018-3627-6 · Cited by 141
Batch Normalization in Convolutional Neural Networks — A comparative study with CIFAR-10 data
Thakkar, V., Tewary, S., & Chakraborty, C. (2018) · International Conference on Emerging Applications of Information Technology · doi:10.1109/eait.2018.8470438 · Cited by 138
Halicek, M. T., Dormer, J. D., Little, J. V., Chen, A. Y., Myers, L. L., Sumer, B. D., & Fei, B. (2019) · Cancers · doi:10.3390/cancers11091367 · Cited by 138
Microscopy cell nuclei segmentation with enhanced U-Net
Long, F. (2020) · BMC Bioinformatics · doi:10.1186/s12859-019-3332-1 · Cited by 138
Transfer learning in magnetic resonance brain imaging: a systematic review
Valverde, J. M., Imani, V., Abdollahzadeh, A., De Feo, R., Prakash, M., Ciszek, R., & Tohka, J. (2021) · Journal of Imaging · doi:10.3390/jimaging7040066 · Cited by 137
Ma, Y., Cao, Y. K., Vrudhula, S. B. K., & Seo, J.-S. (2017) · International Conference on Field Programmable Logic and Applications (FPL) · doi:10.23919/fpl.2017.8056824 · Cited by 137
CRRN: Multi-scale Guided Concurrent Reflection Removal Network
Wan, R., Shi, B., Duan, L., Tan, A., & Kot, A. C. (2018) · 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.2018.00502 · Cited by 137
Liu, T., Huang, J., Liao, T., Pu, R., Liu, S., & Peng, Y. (2022) · IRBM · doi:10.1016/j.irbm.2020.12.002 · Cited by 137
Hooker, S. (2021) · Communications of the ACM · doi:10.1145/3467017 · Cited by 211
Dynamic Video Segmentation Network
Xu, Y.-S., Fu, T.-J., Yang, H.-K., & Lee, C. (2018) · 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.2018.00686 · Cited by 136
3D U-Net Based Brain Tumor Segmentation and Survival Days Prediction
Wang, F., Jiang, R., Zheng, L., Meng, C., & Biswal, B. B. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-46640-4_13 · Cited by 141
Deep learning and its application to medical image segmentationPreprint
Roth, H. R., Shen, C., Oda, H., Oda, M., Hayashi, Y., Misawa, K., & Mori, K. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1803.08691 · Cited by 136
MiniSeg: An Extremely Minimum Network for Efficient COVID-19 Segmentation
Qiu, Y., Liu, Y., Li, S., & Xu, J. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i6.16617 · Cited by 138
Motion Guided Spatial Attention for Video Captioning
Chen, S., & Jiang, Y. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33018191 · Cited by 134
Chang, Y., Chen, J., Qu, C., & Pan, T. (2020) · Renewable Energy · doi:10.1016/j.renene.2020.02.004 · Cited by 134
Citrus pests classification using an ensemble of deep learning models
Khanramaki, M., Asli‐Ardeh, E. A., & Kozegar, E. (2021) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2021.106192 · Cited by 137
A variant form of 3D-UNet for infant brain segmentation
Qamar, S., Jin, H., Zheng, R., Ahmad, P., & Usama, M. (2019) · Future Generation Computer Systems · doi:10.1016/j.future.2019.11.021 · Cited by 133
Metadata
- Authors
- Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A.
- Affiliations
- Google (United States) (US)
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2017-02-12
- Volume
- 31
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2274287116
- Cited by
- 4,636
- References
- 19
- Citations by year
- 2026: 11 · 2025: 95 · 2024: 154 · 2023: 225 · 2022: 260 · 2021: 1149 · 2020: 992 · 2019: 896 · 2018: 543 · 2017: 258
- FWCI
- 211.76
- Type
- conference paper
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
- 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
- Inception-v4, Inception-ResNet, skip connections, image classification, training acceleration, network architecture design, ensemble learning, ImageNet classification
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
