The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Inception and ResNet features are (almost) equivalent
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“According to the experiment from [13], Inception Net extracts all the features captured by Resnet backbone, along with extra features thanks to its multi-scale convolution architecture”
From Combination of facial expressions and EEG for multimodal emotion recognition (2025) · cites this paper as [13]
Citations
Cites
Deep 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
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
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Szegedy, C., Ioffe, S., Vanhoucke, V., & Alemi, A. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v31i1.11231 · Cited by 4,636
Research works printed but not linked: 9
- Erhan, 2009, Visualizing higher-layer features of a deep network, University of Montreal, 1341, 1
- Ioffe, S., & Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167. · arXiv
- Kingma, D. P. & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. · arXiv
- Kornblith, S., Shlens, J., & Le, Q. V. (2018). Do better imagenet models transfer better? arXiv preprint arXiv:1805.08974. · DOI · Paper page
- Lenc, 2019, Understanding image representations by measuring their equivariance and equivalence, International Journal of Computer Vision, 127, 456, 10.1007/s11263-018-1098-y · DOI · Paper page
- Liu, 2015, Crf learning with cnn features for image segmentation, Pattern Recognition, 48, 2983, 10.1016/j.patcog.2015.04.019 · DOI
- Li, 2016, Visual saliency detection based on multiscale deep cnn features, IEEE Transactions on Image Processing, 25, 5012, 10.1109/TIP.2016.2602079 · DOI
- Russakovsky, 2015, Imagenet large scale visual recognition challenge, International Journal of Computer Vision, 115, 211, 10.1007/s11263-015-0816-y · DOI · Paper page
- Zeiler, M.D., Taylor, G.W., & Fergus, R. (2011). Adaptive deconvolutional networks for mid and high level feature learning. In: ICCV, number 2, pp. 6. · DOI
Cited by
Sorted by citations
Jadhav, V., Tiwari, N., & Chawla, M. (2023) · ITM Web of Conferences · doi:10.1051/itmconf/20235302011 · Cited by 11
Combination of facial expressions and EEG for multimodal emotion recognition
Thanh, L. N., & Nguyen, H. T. (2025) · VNU Journal of Science: Computer Science and Communication Engineering · doi:10.25073/2588-1086/vnucsce.4566
“According to the experiment from [13], Inception Net extracts all the features captured by Resnet backbone, along with extra features thanks to its multi-scale convolution architecture; therefore, this study adopted a pretrained InceptionV3 model as a feature extractor.”
Metadata
- Authors
- McNeely-White, D., Beveridge, J. R., & Draper, B. A.
- Affiliations
- Colorado State University (US)
- Corresponding author
- Beveridge, J. Ross
- Venue
- Cognitive Systems Research
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 59
- Pages
- 312-318
- ISSN
- 1389-0417 (ISSN-L)
- OpenAlex
- W2981030558
- Cited by
- 116
- References
- 5
- Citations by year
- 2026: 6 · 2025: 19 · 2024: 21 · 2023: 22 · 2022: 26 · 2021: 15 · 2020: 7
- FWCI
- 5.37
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Cognitive Systems Research (acceptedVersion)
- Funders
- Defense Advanced Research Projects Agency
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- Inception module, ResNet, convolutional neural network architecture, feature similarity, feature mapping, affine transformation, feature evaluation, training data selection, image feature extraction
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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.
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