Model Lineage Analysis: Determination and Closeness Measurement

Area of Science:

• Machine Learning
• Artificial Intelligence
• Computer Science

Background:

• Identifying machine learning model lineage is crucial for understanding model development and cost-efficiency.
• Existing lineage determination methods are empirical, lack theoretical foundations, and struggle with high-impact modifications.
• Measuring the degree of modification (lineage closeness) between models remains an unaddressed challenge.

Purpose of the Study:

• To reformulate model lineage determination based on the loss landscape's local optima.
• To develop a theoretically grounded method for accurate model lineage determination.
• To propose a novel, task-agnostic, and modification-agnostic approach for quantifying lineage closeness.

Main Methods:

• Reframe lineage determination as models' parameters residing in the same loss landscape local optimum.
• Analyze the impact of modifications on decision boundaries to infer lineage closeness.
• Quantify lineage closeness using mean adversarial distance to decision boundaries and prediction matching rates.
• Employ an efficient data point sampling strategy to reduce computational cost.

Main Results:

• Achieved 100% accuracy in model lineage determination across diverse scenarios.
• Provided precise, quantitative measurements of lineage closeness.
• Demonstrated the effectiveness of decision boundary changes as a metric for lineage closeness.

Conclusions:

• The proposed method offers a theoretically sound and highly effective solution for model lineage determination.
• The approach accurately quantifies lineage closeness, addressing a significant gap in current research.
• This work advances the understanding and practical application of model modification techniques.