Efficient Black-Box Knowledge Distillation through Mapping-Emulation

The core message of this paper is to propose a new method called Mapping-Emulation Knowledge Distillation (MEKD) that can effectively distill a black-box cumbersome model into a lightweight model without leaking the internal structure or parameters of the teacher model.