Machine learningDeep learning / NLP / CV

域自适应GAN

域自适应GAN结合了生成对抗学习和域适应技术，旨在弥合标记源域与未标记或稀疏标记目标域之间的分布差距。通过对抗性地训练生成器和判别器，模型学习域不变的表示或转换后的样本，从而使在源数据上训练的分类器或检测器能够有效地泛化到目标域，而无需大量目标标签。

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域自适应GAN

域自适应卷积神经网络领域自适应视觉 Transformer 微调生成对抗网络生成对抗网络 Semi-supervised GAN 迁移学习GAN 域自适应扩散模型

来源

Ganin, Y., Ustunova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., & Lempitsky, V. (2016). Domain-adversarial training of neural networks. Journal of Machine Learning Research, 17(59), 1–35. link ↗
Zhu, J.-Y., Park, T., Isola, P., & Efros, A. A. (2017). Unpaired image-to-image translation using cycle-consistent adversarial networks. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 2223–2232. DOI: 10.1109/ICCV.2017.244 ↗

如何引用本页

ScholarGate. (2026, June 3). Domain-Adaptive Generative Adversarial Network. ScholarGate. https://scholargate.app/zh/deep-learning/domain-adaptive-gan

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域自适应扩散模型迁移学习GAN

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