Generative Adversarial Training for Supervised and Semi-supervised Learning Article Swipe
Xianmin Wang
,
Jing Li
,
Qi Liu
,
Wenpeng Zhao
,
Zuoyong Li
,
Wenhao Wang
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fnbot.2022.859610
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3389/fnbot.2022.859610
Neural networks have played critical roles in many research fields. The recently proposed adversarial training (AT) can improve the generalization ability of neural networks by adding intentional perturbations in the training process, but sometimes still fail to generate worst-case perturbations, thus resulting in limited improvement. Instead of designing a specific smoothness function and seeking an approximate solution used in existing AT methods, we propose a new training methodology, named Generative AT (GAT) in this article, for supervised and semi-supervised learning. The key idea of GAT is to formulate the learning task as a minimax game, in which the perturbation generator aims to yield the worst-case perturbations that maximize the deviation of output distribution, while the target classifier is to minimize the impact of this perturbation and prediction error. To solve this minimax optimization problem, a new adversarial loss function is constructed based on the cross-entropy measure. As a result, the smoothness and confidence of the model are both greatly improved. Moreover, we develop a trajectory-preserving-based alternating update strategy to enable the stable training of GAT. Numerous experiments conducted on benchmark datasets clearly demonstrate that the proposed GAT significantly outperforms the state-of-the-art AT methods in terms of supervised and semi-supervised learning tasks, especially when the number of labeled examples is rather small in semi-supervised learning.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fnbot.2022.859610
- https://www.frontiersin.org/articles/10.3389/fnbot.2022.859610/pdf
- OA Status
- gold
- Cited By
- 5
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4220955967
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4220955967Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fnbot.2022.859610Digital Object Identifier
- Title
-
Generative Adversarial Training for Supervised and Semi-supervised LearningWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-03-24Full publication date if available
- Authors
-
Xianmin Wang, Jing Li, Qi Liu, Wenpeng Zhao, Zuoyong Li, Wenhao WangList of authors in order
- Landing page
-
https://doi.org/10.3389/fnbot.2022.859610Publisher landing page
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https://www.frontiersin.org/articles/10.3389/fnbot.2022.859610/pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.frontiersin.org/articles/10.3389/fnbot.2022.859610/pdfDirect OA link when available
- Concepts
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Computer science, Machine learning, Artificial intelligence, Minimax, Generative grammar, Semi-supervised learning, Adversarial system, Supervised learning, Artificial neural network, Classifier (UML), Mathematical optimization, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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5Total citation count in OpenAlex
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2023: 2, 2022: 3Per-year citation counts (last 5 years)
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36Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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