In this paper, considering the balance of data/model privacy of model owners and user needs, we propose a new setting called Back-Propagated Black-Box Adaptation (BPBA) for users to better train their private models via the guidance of the back-propagated results of a Black-box foundation/source model. Our setting can ease the usage of foundation/source models as well as prevent the leakage and misuse of foundation/source models. Moreover, we also propose a new training strategy called Bootstrap The Original Latent (BTOL) to fully utilize the foundation/source models. Our strategy consists of a domain adapter and a freeze-and-thaw strategy. We apply our BTOL under BPBA and Black-box UDA settings on three different datasets. Experiments show that our strategy is efficient and robust in various settings without manual augmentations.

翻译：在本文中，考虑到模型所有者数据/模型隐私与用户需求之间的平衡，我们提出了一种名为反向传播黑盒自适应（BPBA）的新框架，使用户能够通过黑盒基础/源模型的反向传播结果指导，更好地训练其私有模型。该框架既简化了基础/源模型的使用流程，又可防止其泄露与滥用。此外，我们还提出了一种名为引导原始隐空间（BTOL）的新训练策略，以充分利用基础/源模型。该策略包含域适配器和冻结-解冻策略两个模块。我们在BPBA和黑盒无监督域自适应设置下，针对三个不同数据集应用了BTOL方法。实验表明，在不同设置下，无需手动数据增强，我们的策略均表现出高效性与鲁棒性。