Slice-and-Pack: Tailoring Deep Models for Customized Requirements

Abstract

The learnware paradigm aims to establish a learnware market such that users can build their own models by reusing appropriate existing models in the market without starting from scratch. It is often the case that a single model is insufficient to fully satisfy the user's requirement. Meanwhile, offering multiple models can lead to higher costs for users alongside an increase in hardware resource demands. To address this challenge, this paper proposes the ''Slice-and-Pack'' (S&P) framework to empower the market to provide users with only the required model fragments without having to offer entire abilities of all involved models. Our framework first slices a set of models into small fragments and subsequently packs selected fragments according to user's specific requirement. In the slicing stage, we extract units layer by layer and connect these units to create numerous fragments. In the packing stage, an encoder-decoder mechanism is employed to assemble these fragments. These processes are conducted within data-limited constraints due to privacy concerns. Extensive experiments validate the effectiveness of our framework.