AI RESEARCH
Robust Adaptation of Foundation Models with Black-Box Visual Prompting
arXiv CS.LG
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ArXi:2407.17491v3 Announce Type: replace-cross With a surge of large-scale pre-trained models, parameter-efficient transfer learning (PETL) of large models has garnered significant attention. While promising, they commonly rely on two optimistic assumptions: 1) full access to the parameters of a PTM, and 2) sufficient memory capacity to cache all intermediate activations for gradient computation. However, in most real-world applications, PTMs serve as black-box APIs or