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 301–400 of 1,126
Lightening Network for Low-Light Image Enhancement
Wang, L.-W., Liu, Z.-S., Siu, W.-C., & Lun, D. P.-K. (2020) · IEEE Transactions on Image Processing · doi:10.1109/tip.2020.3008396 · Cited by 287
Deep learning based 2D human pose estimation: A survey
Dang, Q., Yin, J., Wang, B., & Zheng, W. (2019) · Tsinghua Science & Technology · doi:10.26599/tst.2018.9010100 · Cited by 295
Distilling Object Detectors via Decoupled Features
Guo, J., Han, K., Wang, Y., Wu, H., Chen, X., Xu, C., & Xu, C. (2021) · 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/cvpr46437.2021.00219 · Cited by 286
Component Divide-and-Conquer for Real-World Image Super-Resolution
Wei, P., Xie, Z., Lu, H., Zhan, Z., Ye, Q., Zuo, W., & Lin, L. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58598-3_7 · Cited by 279
A novel deep learning method for detection and classification of plant diseases
Albattah, W., Nawaz, M., Javed, A., Masood, M., & Albahli, S. (2021) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00536-1 · Cited by 279
iSAID: A Large-scale Dataset for Instance Segmentation in Aerial ImagesPreprint
Zamir, S. W., Arora, A., Gupta, A., Khan, S. H., Sun, G., Khan, F. S., Zhu, F., Shao, L., Xia, G.-S., & Bai, X. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.12886 · Cited by 284
Hyperparameter Tuning Deep Learning for Diabetic Retinopathy Fundus Image Classification
Shankar, K., Zhang, Y., Liu, Y., Wu, L., & Chen, C. (2020) · IEEE Access · doi:10.1109/access.2020.3005152 · Cited by 278
Large Scale Visual Food Recognition
Min, W., Wang, Z., Liu, Y., Luo, M., Kang, L., Wei, X., Wei, X. K., & Jiang, S. (2023) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2023.3237871 · Cited by 291
Khan, M. A., Javed, K., Khan, S. A., Saba, T., Habib, U., Khan, J. A., & Abbasi, A. A. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-08806-9 · Cited by 277
Towards weeds identification assistance through transfer learning
Espejo-García, B., Mylonas, N., Athanasakos, L., Fountas, S., & VASILAKOGLOU, I. B. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105306 · Cited by 288
A Robust Learning Approach to Domain Adaptive Object Detection
Khodabandeh, M., Vahdat, A., Ranjbar, M., & Macready, W. G. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00057 · Cited by 286
Extremely Low Bit Neural Network: Squeeze the Last Bit Out With ADMM
Leng, C., Dou, Z., Li, H., Shenghuo Zhu, B., & Jin, R. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11713 · Cited by 275
Wei, Y., Tao, R., Wu, Z., Ma, Y., Zhang, L., & Liu, X. (2020) · ACM International Conference on Multimedia (ACM MM) · doi:10.1145/3394171.3413828 · Cited by 280
Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy
Mishra, A., & Marr, D. (2017) · arXiv · doi:10.48550/arxiv.1711.05852 · Cited by 270
Explaining the unique nature of individual gait patterns with deep learning
Horst, F., Lapuschkin, S., Samek, W., Müller, K.-R., & Schöllhorn, W. I. (2019) · Scientific Reports · doi:10.1038/s41598-019-38748-8 · Cited by 295
CIAGAN: Conditional Identity Anonymization Generative Adversarial Networks
Maximov, M., Elezi, I., & Leal-Taixé, L. (2020) · 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/cvpr42600.2020.00549 · Cited by 283
Trends in AI inference energy consumption: Beyond the performance-vs-parameter laws of deep learning
Desislavov, R., Martínez-Plumed, F., & Hernández-Orallo, J. (2023) · Sustainable Computing: Informatics and Systems · doi:10.1016/j.suscom.2023.100857 · Cited by 269
A Deep Learning Framework for Assessing Physical Rehabilitation Exercises
Liao, Y., Vakanski, A., & Xian, M. (2020) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2020.2966249 · Cited by 267
Al-antari, M. A., Han, S.-M., & Kim, T.-S. (2020) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2020.105584 · Cited by 264
Human pose regression by combining indirect part detection and contextual information
Luvizon, D. C., Tabia, H., & Picard, D. (2019) · Computers & Graphics · doi:10.1016/j.cag.2019.09.002 · Cited by 265
Var-CNN: A Data-Efficient Website Fingerprinting Attack Based on Deep Learning
Bhat, S., Lu, D., Kwon, A., & Devadas, S. (2019) · Proceedings on Privacy Enhancing Technologies · doi:10.2478/popets-2019-0070 · Cited by 280
Deep reinforcement learning in computer vision: a comprehensive survey
Le, N., Rathour, V. S., Yamazaki, K., Luu, K., & Savvides, M. (2021) · Artificial Intelligence Review · doi:10.1007/s10462-021-10061-9 · Cited by 271
Prediction of Heart Disease Using Deep Convolutional Neural Networks
Mehmood, A., Iqbal, M., Mehmood, Z., Irtaza, A., Nawaz, M., Nazir, T., & Masood, M. (2021) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-020-05105-1 · Cited by 275
Understanding and Utilizing Deep Neural Networks Trained with Noisy LabelsPreprint
Chen, P., Liao, B., Chen, G., & Zhang, S. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.05040 · Cited by 267
Indoor Person Identification Using a Low-Power FMCW Radar
Vandersmissen, B., Knudde, N., Jalalvand, A., Couckuyt, I., Bourdoux, A., Neve, W. D., & Dhaene, T. (2018) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2018.2816812 · Cited by 262
Fast automated analysis of strong gravitational lenses with convolutional neural networks
Hezaveh, Y. D., Levasseur, L. P., & Marshall, P. J. (2017) · Nature · doi:10.1038/nature23463 · Cited by 261
Computer Vision Techniques in Manufacturing
Zhou, L., Li, Z., & Konz, N. (2022) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2022.3166397 · Cited by 281
Towards a Better Understanding of Transfer Learning for Medical Imaging: A Case Study
Alzubaidi, L., Fadhel, M. A., Al-Shamma, O., Zhang, J., Santamaría, J., Duan, Y., & R. Oleiwi, S. (2020) · Applied Sciences · doi:10.3390/app10134523 · Cited by 258
Co-Learning Feature Fusion Maps From PET-CT Images of Lung Cancer
Kumar, A., FULHAM, M. J., Feng, D., & Kim, J. (2019) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2019.2923601 · Cited by 261
PolyNet: A Pursuit of Structural Diversity in Very Deep Networks
Zhang, X., Li, Z., Loy, C. C., & Lin, D. (2017) · 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.2017.415 · Cited by 266
Byra, M., Galperin, M., Ojeda‐Fournier, H., Olson, L., O'Boyle, M., Comstock, C., & Andre, M. (2019) · Medical Physics · doi:10.1002/mp.13361 · Cited by 253
Transferable Adversarial Perturbations
Zhou, W., Hou, X., Chen, Y., Tang, M., Huang, X., Gan, X., & Yang, Y. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01264-9_28 · Cited by 267
Generative Adversarial Network for Medical Images (MI-GAN)
Iqbal, T., & Ali, H. (2018) · Journal of Medical Systems · doi:10.1007/s10916-018-1072-9 · Cited by 267
Memory-Attended Recurrent Network for Video Captioning
Pei, W., Zhang, J., Wang, X., Ke, L., Shen, X., & Tai, Y. (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.2019.00854 · Cited by 257
Rethinking on Multi-Stage Networks for Human Pose EstimationPreprint
Li, W., Wang, Z., Yin, B., Peng, Q., Du, Y., Xiao, T., Yu, G., Lu, H., Wei, Y., & Sun, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1901.00148 · Cited by 278
DABNet: Depth-wise Asymmetric Bottleneck for Real-time Semantic SegmentationPreprint
Li, G., Yun, I., Kim, J., & Kim, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1907.11357 · Cited by 264
Combining Noise-to-Image and Image-to-Image GANs: Brain MR Image Augmentation for Tumor Detection
Han, C., Rundo, L., Araki, R., Nagano, Y., Furukawa, Y., Mauri, G., Nakayama, H., & Hayashi, H. (2019) · IEEE Access · doi:10.1109/access.2019.2947606 · Cited by 253
Development of convolutional neural network and its application in image classification: a survey
Wang, W., Yang, Y., Wang, X., Wang, W., & Li, J. (2019) · Optical Engineering · doi:10.1117/1.oe.58.4.040901 · Cited by 252
Visual Attention-Driven Hyperspectral Image Classification
Haut, J. M., Paoletti, M. E., Plaza, J., Plaza, A., & Li, J. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2918080 · Cited by 255
Learning a Deep ConvNet for Multi-Label Classification With Partial Labels
Durand, T., Mehrasa, N., & Mori, G. (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.2019.00074 · Cited by 262
Prior-Attention Residual Learning for More Discriminative COVID-19 Screening in CT Images
Wang, J., Bao, Y., Wen, Y., Lu, H., Luo, H., Yunfei, X., Li, X., Liu, C., & Qian, D. (2020) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2020.2994908 · Cited by 253
iNNvestigate neural networks!Preprint
Alber, M., Lapuschkin, S., Seegerer, P., Hägele, M., Schütt, K. T., Montavon, G., Samek, W., Müller, K., Dähne, S., & Kindermans, P.-J. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1808.04260 · Cited by 249
Learning a Dilated Residual Network for SAR Image Despeckling
Zhang, Q., Yuan, Q., Li, J., Yang, Z., & Ma, X. (2018) · Remote Sensing · doi:10.3390/rs10020196 · Cited by 253
A generalized deep learning framework for whole-slide image segmentation and analysis
Khened, M., Kori, A., Rajkumar, H., Krishnamurthi, G., & Srinivasan, B. (2021) · Scientific Reports · doi:10.1038/s41598-021-90444-8 · Cited by 255
Ayala Solares, J. R., Diletta Raimondi, F. E., Zhu, Y., Rahimian, F., Canoy, D., Tran, J., Pinho Gomes, A. C., Payberah, A. H., Zottoli, M., Nazarzadeh, M., et al. (2020) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103337 · Cited by 247
TS-LSTM and temporal-inception: Exploiting spatiotemporal dynamics for activity recognition
Ma, C., Chen, M.-H., Kira, Z., & AlRegib, G. (2018) · Signal Processing Image Communication · doi:10.1016/j.image.2018.09.003 · Cited by 247
Wen, D., Huang, X., Bovolo, F., Li, J., Ke, X., Zhang, A., & Benediktsson, J. A. (2021) · IEEE Geoscience and Remote Sensing Magazine · doi:10.1109/mgrs.2021.3063465 · Cited by 254
Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation
Wu, H., Judd, P., Zhang, X., Isaev, M., & Micikevicius, P. (2020) · arXiv · doi:10.48550/arxiv.2004.09602 · Cited by 246
Generative Adversarial Networks and Its Applications in Biomedical Informatics
Lan, L., You, L., Zhang, Z., Fan, Z., Zhao, W., Zeng, N., Chen, Y., & Zhou, X. (2020) · Frontiers in Public Health · doi:10.3389/fpubh.2020.00164 · Cited by 237
Spatio-Temporal Dynamics and Semantic Attribute Enriched Visual Encoding for Video Captioning
Aafaq, N., Akhtar, N., Liu, W., Gilani, S. Z., & Mian, A. S. (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.2019.01277 · Cited by 249
García, Á. A., Álvarez-García, J. A., & Morillo, L. M. S. (2018) · Neural Networks · doi:10.1016/j.neunet.2018.01.005 · Cited by 249
Surface defect classification of steels with a new semi-supervised learning method
Di, H., Ke, X., Peng, Z., & Dongdong, Z. (2019) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2019.01.011 · Cited by 241
ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design
Ma, N., Zhang, X., Zheng, H.-T., & Sun, J. (2018) · arXiv · Cited by 7,344
The Power of Tiling for Small Object Detection
Unel, F. O., Özkalaycı, B., & Çığla, C. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00084 · Cited by 254
Ji, M., Zhang, L., & Wu, Q. (2019) · Information Processing in Agriculture · doi:10.1016/j.inpa.2019.10.003 · Cited by 237
Adversarial Transformation Networks: Learning to Generate Adversarial ExamplesPreprint
Baluja, S., & Fischer, I. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1703.09387 · Cited by 266
Deep Learning for Generic Object Detection: A Survey
Liu, L., Ouyang, W., Wang, X., Fieguth, P., Chen, J., Liu, X., & Pietikäinen, M. (2018) · arXiv · doi:10.48550/arxiv.1809.02165 · Cited by 2,965
Lu, Y., Zhong, A., Li, Q., & Dong, B. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1710.10121 · Cited by 283
Du, X., Cai, Y., Wang, S., & Zhang, L. (2016) · Youth Academic Annual Conference of Chinese Association of Automation (YAC) · doi:10.1109/yac.2016.7804882 · Cited by 243
Dung, C. V., Sekiya, H., Hirano, S., Okatani, T., & Chitoshi, M. I. K. I. (2019) · Automation in Construction · doi:10.1016/j.autcon.2019.02.013 · Cited by 243
Ruamviboonsuk, P., Krause, J., Chotcomwongse, P., Sayres, R., Raman, R., Widner, K., Campana, B. J. L., Phene, S., Hemarat, K., Tadarati, M., et al. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0099-8 · Cited by 233
Yang, J., Ju, J., Guo, L., Ji, B., Shi, S., Yang, Z., Gao, S., Yuan, X., Tian, G., Liang, Y., et al. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2021.12.028 · Cited by 233
Learning to Attack: Adversarial Transformation Networks
Baluja, S., & Fischer, I. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11672 · Cited by 242
Evaluation of deep neural networks for traffic sign detection systems
García, Á. A., Álvarez-García, J. A., & Morillo, L. M. S. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.08.009 · Cited by 239
RecycleNet: Intelligent Waste Sorting Using Deep Neural Networks
Bircanoğlu, C., Atay, M., Beşer, F., Genc, O., & Kızrak, M. A. (2018) · International Conference on Innovations in Intelligent Systems and Applications (INISTA) · doi:10.1109/inista.2018.8466276 · Cited by 233
Automated diagnosis of breast ultrasonography images using deep neural networks
Qi, X., Zhang, L., Yao, C., Pi, Y., Chen, Y., Lv, Q., & Zhang, Y. (2018) · Medical Image Analysis · doi:10.1016/j.media.2018.12.006 · Cited by 238
Recognition of ischaemia and infection in diabetic foot ulcers: Dataset and techniques
Goyal, M., Reeves, N. D., Rajbhandari, S. M., Ahmad, N., Wang, C., & Yap, M. H. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103616 · Cited by 233
Backdoor Attacks Against Deep Learning Systems in the Physical World
Wenger, E., Passananti, J., Bhagoji, A. N., Yao, Y., Zheng, H.-T., & Zhao, B. Y. (2021) · 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/cvpr46437.2021.00614 · Cited by 249
Goh, G. B., Siegel, C., Vishnu, A., Hodas, N. O., & Baker, N. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.06689 · Cited by 251
On Pre-trained Image Features and Synthetic Images for Deep Learning
Hinterstoißer, S., Lepetit, V., Wohlhart, P., & Konolige, K. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-11009-3_42 · Cited by 232
Detection of anaemia from retinal fundus images via deep learning
Mitani, A., Huang, A., Venugopalan, S., Corrado, G. S., Peng, L., Webster, D. R., Hammel, N., Liu, Y., & Varadarajan, A. V. (2019) · Nature Biomedical Engineering · doi:10.1038/s41551-019-0487-z · Cited by 230
Hyperspectral Classification Based on Lightweight 3-D-CNN With Transfer Learning
Zhang, H., Li, Y., Jiang, Y., Wang, P., Shen, Q., & Shen, C. (2019) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2019.2902568 · Cited by 229
POG: Personalized Outfit Generation for Fashion Recommendation at Alibaba iFashion
Chen, W., Huang, P., Xu, J., Guo, X., Guo, C., Sun, F., Li, C., Pfadler, A., Zhao, H., & Zhao, B. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330652 · Cited by 243
A review of lane detection methods based on deep learning
Tang, J., Li, S., & Liu, P. (2020) · Pattern Recognition · doi:10.1016/j.patcog.2020.107623 · Cited by 240
EfficientVMamba: Atrous Selective Scan for Light Weight Visual Mamba
Pei, X., Huang, T., & Xu, C. (2025) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v39i6.32690 · Cited by 229
WRPN: Wide Reduced-Precision Networks
Mishra, A., Nurvitadhi, E., Cook, J. J., & Marr, D. (2017) · arXiv · doi:10.48550/arxiv.1709.01134 · Cited by 227
Kuo, C., Chang, C.-M., Liu, K., Lin, W.-K., Chiang, H., Chung, C.-W., Ho, M.-R., Sun, P.-R., Yang, R.-L., & Chen, K.-T. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0104-2 · Cited by 227
Deep convolutional neural networks with transfer learning for automated brain image classification
Kaur, T., & Gandhi, T. K. (2020) · Machine Vision and Applications · doi:10.1007/s00138-020-01069-2 · Cited by 234
Gas Classification Using Deep Convolutional Neural Networks
Peng, P., Zhao, X., Pan, X., & Ye, W. (2018) · Sensors · doi:10.3390/s18010157 · Cited by 226
AdaFrame: Adaptive Frame Selection for Fast Video Recognition
Wu, Z., Xiong, C., Ma, C., Socher, R., & Davis, L. S. (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.2019.00137 · Cited by 225
Automatic Colon Polyp Detection Using Region Based Deep CNN and Post Learning Approaches
Shin, Y., Qadir, H. A., Aabakken, L., Bergsland, J., & Balasingham, I. (2018) · IEEE Access · doi:10.1109/access.2018.2856402 · Cited by 222
Deep Learning Solutions for Skin Cancer Detection and Diagnosis
Nahata, H., & SINGH, S. P. (2020) · Learning and analytics in intelligent systems · doi:10.1007/978-3-030-40850-3_8 · Cited by 222
Xiao-min, Z., Li, C., Rahaman, M. M., Yao, Y., Ai, S., Sun, C., Wang, Q., Zhang, Y., Li, M., Li, X., et al. (2020) · IEEE Access · doi:10.1109/access.2020.2993788 · Cited by 228
Radiology Objects in COntext (ROCO): A Multimodal Image Dataset
Pelka, O., Koitka, S., Rückert, J., Nensa, F., & Friedrich, C. M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01364-6_20 · Cited by 233
Punn, N. S., & Agarwal, S. (2020) · Applied Intelligence · doi:10.1007/s10489-020-01900-3 · Cited by 225
Marginal Loss for Deep Face Recognition
Deng, J., Zhou, Y., & Zafeiriou, S. P. (2017) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.251 · Cited by 222
Automatic Lung Cancer Prediction from Chest X-ray Images Using the Deep Learning Approach
Ausawalaithong, W., Thirach, A., Marukatat, S., & Wilaiprasitporn, T. (2018) · Biomedical Engineering International Conference · doi:10.1109/bmeicon.2018.8609997 · Cited by 227
Pham, H., Le, T., Tran, D. Q., Ngo, D. T., & Nguyen, H. Q. (2021) · Neurocomputing · doi:10.1016/j.neucom.2020.03.127 · Cited by 234
Semantic Conditioned Dynamic Modulation for Temporal Sentence Grounding in Videos
Yuan, Y., Ma, L., Wang, J., Liu, W., & Zhu, W. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.3038993 · Cited by 219
Region-Based Convolutional Neural Network for Segmenting Text in Epigraphical Images
Preethi, P., & Ramappa, M. H. (2022) · Artificial Intelligence and Applications · doi:10.47852/bonviewaia2202293 · Cited by 219
Learning representations for the early detection of sepsis with deep neural networks
Kam, H. J., & Kim, H. Y. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.08.015 · Cited by 218
Liu, C., Hu, Y., Li, Y., Song, S., & Liu, J. (2017) · doi:10.1145/3132734.3132739 · Cited by 218
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
Du, X., Lin, T.-Y., Jin, P., Ghiasi, G., Tan, M., Cui, Y., Le, Q. V., & Song, X. (2020) · 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/cvpr42600.2020.01161 · Cited by 223
Shorfuzzaman, M., & Hossain, M. S. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2020.107700 · Cited by 217
Toolflows for Mapping Convolutional Neural Networks on FPGAs
Venieris, S. I., Kouris, A., & Bouganis, C.-S. (2018) · ACM Computing Surveys · doi:10.1145/3186332 · Cited by 236
A CNN-based methodology for breast cancer diagnosis using thermal images
Zuluaga-Gómez, J., Masry, Z. A., Benaggoune, K., Meraghni, S., & Noureddine Zerhouni, A. (2020) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2020.1824685 · Cited by 226
Data Augmentation for Skin Lesion Analysis
Perez, F., Vasconcelos, C. N., Avila, S., & Junior, E. A. D. V. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01201-4_33 · Cited by 215
ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network
Rakotonirina, N. C., & Rasoanaivo, A. (2020) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp40776.2020.9054071 · Cited by 215
A fully-automated deep learning pipeline for cervical cancer classification
Alyafeai, Z., & Ghouti, L. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.112951 · Cited by 221
Fully Motion-Aware Network for Video Object Detection
Wang, S., Zhou, Y., Yan, J., & Deng, Z. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01261-8_33 · Cited by 213
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
