Sentinel-Guided Zero-Shot Learning: A Collaborative Paradigm without
  Real Data Exposure
Fan Wan Xingyu Miao Haoran Duan Jingjing Deng Rui Gao Yang Long
With increasing concerns over data privacy and model copyrights, especially
in the context of collaborations between AI service providers and data owners,
an innovative SG-ZSL paradigm is proposed in this work. SG-ZSL is designed to
foster efficient collaboration without the need to exchange models or sensitive
data. It consists of a teacher model, a student model and a generator that
links both model entities. The teacher model serves as a sentinel on behalf of
the data owner, replacing real data, to guide the student model at the AI
service provider's end during training. Considering the disparity of knowledge
space between the teacher and student, we introduce two variants of the teacher
model: the omniscient and the quasi-omniscient teachers. Under these teachers'
guidance, the student model seeks to match the teacher model's performance and
explores domains that the teacher has not covered. To trade off between privacy
and performance, we further introduce two distinct security-level training
protocols: white-box and black-box, enhancing the paradigm's adaptability.
Despite the inherent challenges of real data absence in the SG-ZSL paradigm, it
consistently outperforms in ZSL and GZSL tasks, notably in the white-box
protocol. Our comprehensive evaluation further attests to its robustness and
efficiency across various setups, including stringent black-box training
protocol.