The DOI matches its Crossref record; 27 of the 32 references it cites were found in a registry or an index.
Generative OpenMax for Multi-Class Open Set Classification
Open accessAbstract
We present a conceptually new and flexible method for multi-class open set classification. Unlike previous methods where unknown classes are inferred with respect to the feature or decision distance to the known classes, our approach is able to provide explicit modelling and decision score for unknown classes. The proposed method, called Gener- ative OpenMax (G-OpenMax), extends OpenMax by employing generative adversarial networks (GANs) for novel category image synthesis. We validate the proposed method on two datasets of handwritten digits and characters, resulting in superior results over previous deep learning based method OpenMax Moreover, G-OpenMax provides a way to visualize samples representing the unknown classes from open space. Our simple and effective approach could serve as a new direction to tackle the challenging multi-class open set classification problem.
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“A particularly fruitful approach seems to be the use of Generative Adversarial Networks (GANs) for detecting out-of-sample cases.[24][25][26]”
From The need for uncertainty quantification in machine-assisted medical decision making (2019) · cites this paper as [26]
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 · cited 1×
“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].”
BackgroundImageNet 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 · cited 2×
“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].”
BackgroundMethodImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · cited 4×
“We trained the generator G on ImageNet12 and made several observations: 1) Several classes blend into unrealistic type of object in terms of colour and global structure.”
BackgroundInfluentialImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · cited 1×
“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].”
BackgroundImage-to-Image Translation with Conditional Adversarial Networks
Isola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.632 · Cited by 22,930 · cited 1×
“GAN has a wide range of applications including, sketch/text to image synthesis [4], image to image translation [10], image editing [19], super resolution [15] and video prediction [6], etc.”
BackgroundAuto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067 · cited 2×
“The most prominent are variational autoencoder (VAE) [12] and generative adversarial networks (GAN) [7], among which, GAN is a powerful model of learning arbitrarily complex data distributions.”
BackgroundPhoto-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696 · cited 1×
“GAN has a wide range of applications including, sketch/text to image synthesis [4], image to image translation [10], image editing [19], super resolution [15] and video prediction [6], etc.”
BackgroundUnsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., & Chintala, S. (2015) · arXiv · doi:10.48550/arxiv.1511.06434 · Cited by 12,235
Improved Techniques for Training GANs
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016) · arXiv · doi:10.48550/arxiv.1606.03498 · Cited by 6,965 · cited 2×
“This is done by using generative adversarial networks (GANs) [7] to generate synthetic samples from unknown classes.”
BackgroundInception-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 · cited 1×
“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].”
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5 of the 32 references couldn’t be matched to a known paper.
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Sorted by citations
Recent Advances in Open Set Recognition: A Survey
Geng, C., Huang, S.-J., & Chen, S. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2020.2981604 · Cited by 1,106
“Due to the properties of AL, some researchers also attempt to account for open space with the unknown unknown classes generated by the AL technique [78]–[81].”
BackgroundMethodInfluentialThe need for uncertainty quantification in machine-assisted medical decision making
Begoli, E., Bhattacharya, T., & Kusnezov, D. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-018-0004-1 · Cited by 597
Open Set Domain Adaptation by Backpropagation
Saito, K., Yamamoto, S., Ushiku, Y., & Harada, T. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-01228-1_10 · Cited by 603
“Moreover, to give supervision of the unknown samples, a method to generate these samples was proposed [26].”
MethodTowards Open World Object Detection
Joseph, K. J., Khan, S. H., Khan, F. S., & Balasubramanian, V. N. (2021) · 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/cvpr46437.2021.00577 · Cited by 606
“A generative version of OpenMax was proposed in [13] by synthesizing novel class images.”
BackgroundGeneralized Out-of-Distribution Detection: A Survey
Yang, J., Zhou, K., Li, Y., & Liu, Z. (2024) · International Journal of Computer Vision · doi:10.1007/s11263-024-02117-4 · Cited by 884
“[160] [180] [181] [153] [154] [155] [173] [183] [184]”
Metadata
- Authors
- Ge, Z., Demyanov, S., & Garnavi, R.
- Venue
- Procedings of the British Machine Vision Conference 2017
- Publisher
- British Machine Vision Association
- Published
- 2017
- OpenAlex
- W2963875483
- Cited by
- 472
- References
- 32
- Citations by year
- 2026: 36 · 2025: 59 · 2024: 98 · 2023: 90 · 2022: 63 · 2021: 74 · 2020: 31 · 2019: 12 · 2018: 8 · 2017: 1
- FWCI
- 4.16
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- British Machine Vision Conference (BMVC) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- 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 › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Keywords
- open-set recognition, OpenMax, generative adversarial networks, image generation, unknown class detection, handwritten digits, handwritten characters
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