Encapsulating Knowledge in One Prompt

TL;DR

This paper introduces KiOP, a novel paradigm for knowledge transfer that encapsulates information into a single prompt, enabling efficient, data-free, and parallel knowledge transfer across multiple models without modifying original models.

Contribution

It demonstrates the effectiveness of Visual Prompt for data-inaccessible knowledge transfer and addresses issues of reusability and storage in traditional data-free methods.

Findings

Effective knowledge transfer without training data

Supports multiple models simultaneously

Reduces storage and reusability issues

Abstract

This paradigm encapsulates knowledge from various models into a solitary prompt without altering the original models or requiring access to the training data, which enables us to achieve efficient and convenient knowledge transfer in more realistic scenarios. From a practicality standpoint, this paradigm not only for the first time proves the effectiveness of Visual Prompt in data inaccessible contexts, but also solves the problems of low model reusability and high storage resource consumption faced by traditional Data-Free Knowledge Transfer, which means that we can realize the parallel knowledge transfer of multiple models without modifying any source model. Extensive experiments across various datasets and models demonstrate the efficacy of the proposed KiOP knowledge transfer paradigm. Without access to real training data and with rigorous storage capacity constraints, it is also capable of yielding considerable outcomes when dealing with cross-model backbone setups and handling parallel knowledge transfer processing requests with multiple (more than 2) models.