The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Loss-Sensitive Generative Adversarial Networks on Lipschitz Densities
Open accessAbstract
In this paper, we present the Lipschitz regularization theory and algorithms for a novel Loss-Sensitive Generative Adversarial Network (LS-GAN). Specifically, it trains a loss function to distinguish between real and fake samples by designated margins, while learning a generator alternately to produce realistic samples by minimizing their losses. The LS-GAN further regularizes its loss function with a Lipschitz regularity condition on the density of real data, yielding a regularized model that can better generalize to produce new data from a reasonable number of training examples than the classic GAN. We will further present a Generalized LS-GAN (GLS-GAN) and show it contains a large family of regularized GAN models, including both LS-GAN and Wasserstein GAN, as its special cases. Compared with the other GAN models, we will conduct experiments to show both LS-GAN and GLS-GAN exhibit competitive ability in generating new images in terms of the Minimum Reconstruction Error (MRE) assessed on a separate test set. We further extend the LS-GAN to a conditional form for supervised and semi-supervised learning problems, and demonstrate its outstanding performance on image classification tasks.
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Citations
Cites
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,395
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
GAN（Generative Adversarial Nets）
(2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 19,270
Unsupervised 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
Conditional Generative Adversarial Nets
Mirza, M., & Osindero, S. (2014) · arXiv · doi:10.48550/arxiv.1411.1784 · Cited by 8,863
Tarvainen, A., & Valpola, H. (2017) · arXiv · doi:10.48550/arxiv.1703.01780 · Cited by 4,275
Reading digits in natural images with unsupervised feature learning
Netzer, Y. (2024) · doi:10.57702/e7wwo0zk · Cited by 4,149
The Lack of A Priori Distinctions Between Learning Algorithms
Wolpert, D. H. (1996) · Neural Computation · doi:10.1162/neco.1996.8.7.1341 · Cited by 1,647
Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
Denton, E., Chintala, S., Szlam, A. D., & Fergus, R. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.05751 · Cited by 1,541
Semi-Supervised Learning with Deep Generative Models
Kingma, D. P., Rezende, D. J., Mohamed, S., & Welling, M. (2014) · UvA-DARE (University of Amsterdam) · doi:10.48550/arxiv.1406.5298 · Cited by 1,415
Cited by
“Least squares generative adversarial networks,”
Mao, X., Li, Q., Xie, H., Lau, R. Y. K., Wang, Z., & Smolley, S. P. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.304 · Cited by 5,437
Spectral Normalization for Generative Adversarial Networks
Miyato, T., Kataoka, T., Koyama, M., & Yoshida, Y. (2018) · arXiv · doi:10.48550/arxiv.1802.05957 · Cited by 3,438
“A number of works (Ue-hara et al., 2016; Qi, 2017; Gulrajani et al., 2017) advocate the importance of Lipschitz continuity in assuring the boundedness of statistics.”
BackgroundInfluentialDeep Learning in Mobile and Wireless Networking: A Survey
Zhang, C., Patras, P., & Haddadi, H. (2019) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2019.2904897 · Cited by 1,768
A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
Gui, J., Sun, Z., Wen, Y., Tao, D., & Ye, J. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3130191 · Cited by 1,231
Generative Adversarial Networks (GANs): Challenges, Solutions, and Future Directions
Saxena, D., & Cao, J. (2022) · ACM Computing Surveys · doi:10.1145/3446374 · Cited by 689
Metadata
- Authors
- Qi, G.-J.
- Venue
- International Journal of Computer Vision
- Published
- 2019-11-27
- Volume
- 128
- Issue
- 5
- Pages
- 1118-1140
- ISSN
- 0920-5691 (ISSN-L)
- 1573-1405
- OpenAlex
- W2991327923
- Cited by
- 341
- References
- 25
- FWCI
- 22.57
- Type
- journal article
- Language
- en
- Open access
- green
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