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 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.
“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]
The Experimental Results section says pre-trained weights for the paper’s models are available, and the Introduction discusses applying convolutional architectures to object detection. Neither passage establishes that most vehicle-detection systems use pre-trained weights or that those weights come from MS-COCO; the paper instead identifies ILSVRC12 as its benchmark. Page numbers are unavailable in our copy. (LLM, conf=0.82)
Experimental — accuracy not yet measuredUsing a pre-trained model trained on ground-viewpoint images such as MS-COCO and ImageNet leads to weak performance in detecting vehicles from UAVs because the captured images have different characteristics.
“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]
The Introduction and Experimental Results describe Inception models trained and evaluated on the ImageNet/ILSVRC benchmark. They do not assess UAV vehicle detection or say that differences between ground-view and UAV images cause weak performance. (LLM, conf=0.86)
Experimental — accuracy not yet measuredMulti-scale convolutional layer and convolution of length 1 enable IENet to extract rich high-dimensional features with limited parameters.
“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]
The Residual Inception Blocks section describes a 1 × 1 convolution that expands filter-bank dimensionality, and the Related Work section says a 1 × 1 convolution can reduce computational cost (page numbers unavailable). The paper also says its Inception models keep parameter counts in check, but it does not evaluate IENet or establish that these layers enable IENet to extract rich features with limited parameters. (LLM, conf=0.82)
Experimental — accuracy not yet measuredSzegedy et al. proposed Inception-ResNet and Inception-V3/4 as upgraded types of Inception-V1/2.
“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 measuredSince drone images have aerial views with occlusions, varying sizes and orientations, employing DL models pre-trained on datasets such as Imagenet and MS-COCO might not provide high performance.
“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]
The Introduction (page unavailable) identifies ImageNet as an image-recognition benchmark, and the Experimental Results report performance on that benchmark. Our copy does not address drone imagery, its occlusions or varied viewpoints, or whether ImageNet- or MS-COCO-pre-trained models perform poorly on drone images. Searches for those terms and for aerial-image transfer learning found no supporting passage. (LLM, conf=0.76)
Experimental — accuracy not yet measured
By applying convolutions of different lengths in the convolutional layer, IENet is capable of generating long-term and short-term features of EEG simultaneously to improve feature diversity.
“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]
The Introduction and Architectural Choices sections discuss Inception networks for image recognition, but do not address IENet or EEG. Searches for different convolution lengths and for long- and short-term EEG features found no passage supporting the claimed effect on feature diversity. (LLM, conf=0.76)
Experimental — accuracy not yet measuredIGCV2 and IGCV3 are slow due to usage of too many convolution groups (4 or 8).
“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]
The Residual Inception Blocks section discusses the step time of Inception-v4, attributing its slower performance to a larger number of layers. Searches for IGCV2, IGCV3, and convolution groups found no passage in our copy addressing whether those models are slow or use 4 or 8 groups. No page numbers are available in the indexed copy. (LLM, conf=0.78)
Experimental — accuracy not yet measured
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Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
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Self-Training With Noisy Student Improves ImageNet Classification
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Object detection in optical remote sensing images: A survey and a new benchmark
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Simple Baselines for Human Pose Estimation and Tracking
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Ensemble Adversarial Training: Attacks and Defenses
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Deep Facial Expression Recognition: A Survey
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LinkNet: Exploiting encoder representations for efficient semantic segmentation
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Digital twin: values, challenges and enablers from a modeling perspective
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EuroSAT: A Novel Dataset and Deep Learning Benchmark for Land Use and Land Cover Classification
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A Survey of Autonomous Driving: <i>Common Practices and Emerging Technologies</i>
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Receptive Field Block Net for Accurate and Fast Object Detection
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Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning
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Cascaded Pyramid Network for Multi-person Pose Estimation
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BiSeNet V2: Bilateral Network with Guided Aggregation for Real-Time Semantic Segmentation
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Inceptiontime: finding alexnet for time series classification
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Adversarial Examples: Attacks and Defenses for Deep Learning
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Wider or Deeper: Revisiting the ResNet Model for Visual Recognition
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Single-Shot Refinement Neural Network for Object Detection
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Harmonious Attention Network for Person Re-identification
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The iNaturalist Species Classification and Detection Dataset
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Focal Loss for Dense Object Detection
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Attention Augmented Convolutional Networks
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Disentangling and Unifying Graph Convolutions for Skeleton-Based Action Recognition
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Sanity Checks for Saliency Maps
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CornerNet: Detecting Objects as Paired Keypoints
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Deep Variational Information BottleneckPreprint
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Self-citationDo Better ImageNet Models Transfer Better?
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- 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 · 2016: 50 · 2012: 2
- 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 ›
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