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.
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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]
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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]
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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]
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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]
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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
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Going deeper with convolutions
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Self-citationImageNet Large Scale Visual Recognition Challenge
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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
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Yurtsever, E., Liu, Y., Lambert, J., Miyajima, C., Takeuchi, E., Takeda, K., & Hansen, J. H. L. (2019) · 2019 IEEE Intelligent Transportation Systems Conference (ITSC) · doi:10.1109/itsc.2019.8917362 · Cited by 40
IENet: a robust convolutional neural network for EEG based brain-computer interfaces
Du, Y., & Liu, J. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac7257 · Cited by 34
“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].”
MethodResultInfluentialORBIT: A Real-World Few-Shot Dataset for Teachable Object Recognition
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“There is empirical evidence that combining the Inception architecture with residual connections can outperform similarly expensive Inception networks without residual connections [43].”
BackgroundRecognizing Human Races through Machine Learning—A Multi-Network, Multi-Features Study
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Talib, M. A., Nasir, Q., Nassif, A. B., Fadhl, N. B., & Gouda, O. M. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-20618-3 · Cited by 5
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
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