  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.
