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.

在这份研究中，我们提出了一种创新的 SG-ZSL 范式，旨在解决数据隐私和模型版权等问题，该范式通过引入教师模型、学生模型和连接两者的生成器来促进高效协作，无需交换模型或敏感数据。在教师模型的指导下，学生模型通过匹配教师模型的性能并探索教师未涵盖的领域来实现训练，通过白盒和黑盒两个不同的安全级别训练协议，实现了隐私与性能之间的平衡，并在 ZSL 和 GZSL 任务中持续取得优越的表现，尤其在白盒协议中。我们在不同设置下进行了全面评估，证明了其在各种情况下的鲁棒性和效率。