尽管看起来不可思议，但迁移学习实际上还不是图像合成的默认技术。
本文中介绍了一种通过生成知识转移来训练Vision Transformer的方法。 我们展示了 STOA 在具有截然不同数量的训练图像的各种视觉域上的可迁移性
As unlikely as it may seem, transfer learning is not yet a de facto technique for image synthesis. In this paper, we present a recipe for learning vision transformers by generative knowledge transfer. We show the STOA transfer-ability on a variety of visual domains with drastically different amounts of training images.
Visual Prompt Tuning for Generative Transfer Learning
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