  Large pretrained visual models exhibit remarkable generalization across
diverse recognition tasks. Yet, real-world applications often demand compact
models tailored to specific problems. Variants of knowledge distillation have
been devised for such a purpose, enabling task-specific compact models (the
students) to learn from a generic large pretrained one (the teacher). In this
paper, we show that the excellent robustness and versatility of recent
pretrained models challenge common practices established in the literature,
calling for a new set of optimal guidelines for task-specific distillation. To
address the lack of samples in downstream tasks, we also show that a variant of
Mixup based on stable diffusion complements standard data augmentation. This
strategy eliminates the need for engineered text prompts and improves
distillation of generic models into streamlined specialized networks.
