Improving Diversity in Black-box Few-shot Knowledge Distillation
Knowledge distillation (KD) is a well-known technique to effectively compress a large network (teacher) to a smaller network (student) with little sacrifice in performance. However, most KD methods require a large training set and internal access to the teacher, which are rarely available due to various restrictions. These challenges have originated a more practical setting known as black-box few-shot KD, where the student is trained with few images and a black-box teacher. Recent approaches typically generate additional synthetic images but lack an active strategy to promote their diversity, a crucial factor for student learning. To address these problems, we propose a novel training sch...
| Authors | Tri-Nhan Vo, Dang Nguyen, Kien Do et al. |
|---|---|
| Primary category | cs.CV |
| Published | 28 April 2026 |
| Last updated at source | 28 April 2026 |
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