Encapsulating Knowledge in One Prompt
This addresses low model reusability and high storage consumption in data-free knowledge transfer for AI/ML applications, though it appears incremental as it builds on existing visual prompt concepts.
The paper tackles the problem of knowledge transfer without access to training data or modifying source models by encapsulating knowledge from multiple models into a single prompt, achieving efficient parallel transfer across various datasets and models under storage constraints.
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.