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Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
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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 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]
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“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]
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“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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“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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“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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“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]
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“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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1 retracted
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Recent progress in semantic image segmentation
Liu, X., Deng, Z., & Yang, Y. (2018) · Artificial Intelligence Review · doi:10.1007/s10462-018-9641-3 · Cited by 538
FPGA-Based Accelerators of Deep Learning Networks for Learning and Classification: A Review
Shawahna, A., Sait, S. M., & El-Maleh, A. H. (2018) · IEEE Access · doi:10.1109/access.2018.2890150 · Cited by 539
A scoping review of transfer learning research on medical image analysis using ImageNet
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CleanNet: Transfer Learning for Scalable Image Classifier Training with Label Noise
Lee, K.-H., He, X., Zhang, L., & Yang, L. (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.2018.00571 · Cited by 515
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“Driven by recent research in machine learning, the best performing system can achieve impressive results on both small and large scale multi-class classification tasks [8, 9, 13, 21, 28].”
BackgroundNonparametric regression using deep neural networks with ReLU activation function
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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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