BlackVIP: Black-Box Visual Prompting for Robust Transfer Learning

Understand

With the surge of large-scale pre-trained models (PTMs), fine-tuning these models to numerous downstream tasks becomes a crucial problem.

• Consequently, parameter efficient transfer learning (PETL) of large models has grasped huge attention.
• While recent PETL methods showcase impressive performance, they rely on optimistic assumptions: 1) the entire parameter set of a PTM is available, and 2) a sufficiently large memory capacity for the fine-tuning is equipped.
• However, in most real-world applications, PTMs are served as a black-box API or proprietary software without explicit parameter accessibility.