Absolute Zero-Shot Learning

• URL: http://arxiv.org/abs/2202.11319v1
• Date: Wed, 23 Feb 2022 05:33:21 GMT
• Title: Absolute Zero-Shot Learning
• Abstract summary: The key innovation is to involve a teacher model as the data safeguard to guide the AZSL model training without data leaking. We investigate black-box' and white-box' scenarios in AZSL task as different levels of model security. Our framework can retain state-of-the-art ZSL and GZSL performance under the white-box' scenario.
• Score: 14.187187391391882
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Considering the increasing concerns about data copyright and privacy issues, we present a novel Absolute Zero-Shot Learning (AZSL) paradigm, i.e., training a classifier with zero real data. The key innovation is to involve a teacher model as the data safeguard to guide the AZSL model training without data leaking. The AZSL model consists of a generator and student network, which can achieve date-free knowledge transfer while maintaining the performance of the teacher network. We investigate `black-box' and `white-box' scenarios in AZSL task as different levels of model security. Besides, we also provide discussion of teacher model in both inductive and transductive settings. Despite embarrassingly simple implementations and data-missing disadvantages, our AZSL framework can retain state-of-the-art ZSL and GZSL performance under the `white-box' scenario. Extensive qualitative and quantitative analysis also demonstrates promising results when deploying the model under `black-box' scenario.

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