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微调生成对抗网络

微调的GAN从一个大型预训练生成对抗网络开始，并在较小的目标数据集上继续进行对抗训练，从而使模型能够在新领域合成高质量样本，而无需从头开始训练。这种迁移方法显著降低了数据和计算需求，同时保留了预训练期间学到的丰富特征表示。

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微调生成对抗网络
微调卷积神经网络微调扩散模型微调变分自编码器微调视觉Transformer生成对抗网络迁移学习GAN域自适应GAN迁移学习与变分自编码器

来源

  1. 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 ↗
  2. Mo, S., Cho, M., & Shin, J. (2020). Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs. CVPR 2020 Workshop on AI for Content Creation. link ↗

如何引用本页

ScholarGate. (2026, June 3). Fine-Tuned Generative Adversarial Network (Domain-Adaptive GAN via Transfer). ScholarGate. https://scholargate.app/zh/deep-learning/fine-tuned-generative-adversarial-network

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被引用于

域自适应GAN微调扩散模型微调变分自编码器迁移学习GAN迁移学习与变分自编码器

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ScholarGate — Fine-Tuned Generative Adversarial Network (Fine-Tuned Generative Adversarial Network (Domain-Adaptive GAN via Transfer)). 于 2026-06-15 检索自 https://scholargate.app/zh/deep-learning/fine-tuned-generative-adversarial-network · 数据集： https://doi.org/10.5281/zenodo.20539026