Handling Learnwares from Heterogeneous Feature and Label Spaces with Explicit Label Exploitation

Area of Science:

• Artificial Intelligence
• Machine Learning
• Software Engineering

Background:

• The learnware paradigm promotes model reuse over training from scratch.
• Existing systems struggle with heterogeneous feature and label spaces.
• Current specifications inadequately utilize crucial label information, particularly model outputs.

Purpose of the Study:

• To develop a unified representation for comparing heterogeneous models.
• To enhance learnware reusability beyond original task specifications.
• To enable effective model matching using model outputs.

Main Methods:

• Evolving specifications using model outputs into a unified space.
• Constructing a unified capability representation of heterogeneous models.
• Matching conditional distributions of model outputs with user task distributions.

Main Results:

• The proposed method effectively unifies heterogeneous model capabilities.
• Learnware identification and assembly are improved across diverse spaces.
• Successful reuse of models is demonstrated even without explicit task tailoring.

Conclusions:

• Model outputs are critical for unifying heterogeneous model representations.
• The approach significantly enhances learnware discoverability and reusability.
• This work advances the practical application of the learnware paradigm.