  Data-Free Knowledge Distillation (DFKD) has shown great potential in creating
a compact student model while alleviating the dependency on real training data
by synthesizing surrogate data. However, prior arts are seldom discussed under
distribution shifts, which may be vulnerable in real-world applications. Recent
Vision-Language Foundation Models, e.g., CLIP, have demonstrated remarkable
performance in zero-shot out-of-distribution generalization, yet consuming
heavy computation resources. In this paper, we discuss the extension of DFKD to
Vision-Language Foundation Models without access to the billion-level
image-text datasets. The objective is to customize a student model for
distribution-agnostic downstream tasks with given category concepts, inheriting
the out-of-distribution generalization capability from the pre-trained
foundation models. In order to avoid generalization degradation, the primary
challenge of this task lies in synthesizing diverse surrogate images driven by
text prompts. Since not only category concepts but also style information are
encoded in text prompts, we propose three novel Prompt Diversification methods
to encourage image synthesis with diverse styles, namely Mix-Prompt,
Random-Prompt, and Contrastive-Prompt. Experiments on out-of-distribution
generalization datasets demonstrate the effectiveness of the proposed methods,
with Contrastive-Prompt performing the best.
