Video to Video Generative Adversarial Network for Few-Shot Learning Based on Policy Gradient
- DOI
- 10.1109/tnnls.2025.3616322
- Published
- 2026-03
- Container
- IEEE Transactions on Neural Networks and Learning Systems
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Open access
- unknown
Credibility signals
uncertain Score 64/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.
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- supportingMetadata completeness: All 6 scored descriptive metadata groups are present.
Cite this work
BibTeX
@article{allodium:10.1109/tnnls.2025.3616322,
title = {Video to Video Generative Adversarial Network for Few-Shot Learning Based on Policy Gradient},
author = {Yintai Ma and Diego Klabjan and Jean Utke},
year = {2026},
journal = {IEEE Transactions on Neural Networks and Learning Systems},
doi = {10.1109/tnnls.2025.3616322},
url = {https://doi.org/10.1109/tnnls.2025.3616322}
}RIS
TY - JOUR TI - Video to Video Generative Adversarial Network for Few-Shot Learning Based on Policy Gradient AU - Yintai Ma AU - Diego Klabjan AU - Jean Utke PY - 2026 JO - IEEE Transactions on Neural Networks and Learning Systems DO - 10.1109/tnnls.2025.3616322 UR - https://doi.org/10.1109/tnnls.2025.3616322 ER -
APA
Ma, Y., Klabjan, D., & Utke, J. (2026). Video to Video Generative Adversarial Network for Few-Shot Learning Based on Policy Gradient. IEEE Transactions on Neural Networks and Learning Systems. https://doi.org/10.1109/tnnls.2025.3616322
Source records
- crossref · retrieved 2026-09-25T20:27:41.850Z