方法对比
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| 域自适应GAN× | 微调生成对抗网络× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2016–2017 | 2014 (GAN); 2019–2020 (fine-tuning paradigm) |
| 提出者≠ | Ganin et al. (DANN); Zhu et al. (CycleGAN) | Goodfellow, I. et al. (GAN); fine-tuning practice established ~2019–2020 |
| 类型≠ | Generative adversarial model with domain adaptation | Generative model (adversarial training + transfer) |
| 开创性文献≠ | 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 ↗ | Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative Adversarial Nets. Advances in Neural Information Processing Systems (NeurIPS), 27. link ↗ |
| 别名 | DA-GAN, domain adaptation GAN, adversarial domain adaptation, domain-adaptive generative adversarial network | Fine-Tuned GAN, GAN Fine-Tuning, Domain-Adapted GAN, Transfer GAN |
| 相关 | 6 | 6 |
| 摘要≠ | A Domain-Adaptive GAN combines generative adversarial learning with domain adaptation to bridge the distribution gap between a labeled source domain and an unlabeled or sparsely labeled target domain. By training a generator and discriminator adversarially, the model learns domain-invariant representations or translated samples, enabling a classifier or detector trained on source data to generalize effectively to the target domain without requiring abundant target labels. | A Fine-Tuned GAN starts from a large pre-trained generative adversarial network and continues adversarial training on a smaller target dataset, allowing the model to synthesize high-quality samples in a new domain without training from scratch. This transfer approach dramatically reduces data and compute requirements while preserving the rich feature representations learned during pre-training. |
| ScholarGate数据集 ↗ |
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