The DOI matches its Crossref record; 19 of the 19 references it cites were found in a registry or an index.
Transfer learning for automated optical inspection
Abstract
One of the challenges in applying convolutional neural networks to automated optical inspection is the lack of sufficient training data. In this paper we show that transfer learning can be successfully applied using image data from an entirely different domain. Focusing on optical inspection of texture images, we transfer weights from a source network trained with arbitrary unrelated images from the ImageNet dataset. Inspection experiments using our method show that one epoch of fine-tuning is sufficient to achieve 99.95% classification accuracy, while conventional transfer learning without fine-tuning achieves only 78.76%. An in-depth analysis of the effects of fine-tuning reveals that after fine-tuning, most of the unnecessary features encoded in the weights of the source network are deactivated, while meaningful features of the target data are amplified to capture new variations in the target domain.
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In the citing paper
“Author [21] showed that transfer learning can be successfully applied using image data from an entirely different domain.”
From An Improved VGG19 Transfer Learning Strip Steel Surface Defect Recognition Deep Neural Network Based on Few Samples and Imbalanced Datasets (2021) · cites this paper as [21]
In the citing paper
“In the practice of surface inspection [15], a fine-tuned ImageNet pretrained VGG-16 is used as a patch classifier on surface defect inspection. The experiment shows that fine-tuning all layers is the best valid way for models to achieve high accuracy.”
From A Compact Convolutional Neural Network for Surface Defect Inspection (2020) · cites this paper as [15]
In the citing paper
“Rich features learned from ImageNet push the VGG-16 well-fit to the DAGM and get the best mAcc(99.93%) overall.”
From A Compact Convolutional Neural Network for Surface Defect Inspection (2020) · cites this paper as [15]
In the citing paper
“However, the parameters of a VGG-16 are 134.31 M, which causes very large computational cost—9024 M FLOPs in one sub-image, 9 times FLOPs are needed in an entire input image.”
From A Compact Convolutional Neural Network for Surface Defect Inspection (2020) · cites this paper as [15]
Citations
Cites
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
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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
Visualizing and understanding convolutional networks
Zeiler, M. D., & Fergus, R. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-10590-1_53 · Cited by 15,839
How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., & Lipson, H. (2014) · arXiv · doi:10.48550/arxiv.1411.1792 · Cited by 6,061
Shin, H.-C., Roth, H. R., Gao, M., Lu, L., Xu, Z., Nogues, I., Yao, J., Mollura, D., & Summers, R. M. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2528162 · Cited by 5,759
CNN Features Off-the-Shelf: An Astounding Baseline for Recognition
Razavian, A. S., Azizpour, H., Sullivan, J., & Carlsson, S. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops · doi:10.1109/cvprw.2014.131 · Cited by 4,451
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., & Darrell, T. (2013) · arXiv · doi:10.48550/arxiv.1310.1531 · Cited by 4,019
Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks
Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.222 · Cited by 3,150
Understanding Neural Networks Through Deep VisualizationPreprint
Yosinski, J., Clune, J., Nguyen, A. M., Fuchs, T. J., & Lipson, H. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.06579 · Cited by 1,918
Flagged first, then by citations
Research works printed but not linked: 1
- erhan, 2009, Visualizing Higher-layer Features of Deep Networks, 1341
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Items 101–101 of 101
Chen, G., Jiang, F., Zhang, S., Zhong, Z., Wu, Z., & Li, W. (2026) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2026.3667361
Metadata
- Authors
- Kim, S., Kim, W., Noh, Y.-K., & Park, F. C.
- Affiliations
- Seoul National University (KR)
- Venue
- 2017 International Joint Conference on Neural Networks (IJCNN)
- Publisher
- IEEE
- Published
- 2017-05
- Pages
- 2517-2524
- ISSN
- 2161-4407 (ISSN-L)
- OpenAlex
- W2735783528
- Cited by
- 101
- References
- 19
- Citations by year
- 2026: 1 · 2025: 7 · 2024: 13 · 2023: 18 · 2022: 23 · 2021: 13 · 2020: 12 · 2019: 10 · 2018: 4
- FWCI
- 10.44
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Joint Conference on Neural Networks (IJCNN) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- transfer learning, automated optical inspection, fine-tuning, convolutional neural networks, ImageNet, texture images, limited training data, domain shift
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