Understanding Knowledge Transferability for Transfer Learning: A Survey

2025-07-03

AI Summary

This study addresses the **reliable assessment of knowledge transferability** in transfer learning—a longstanding challenge hindered by inconsistent evaluation criteria, poor interpretability, and ill-defined applicability scopes. We propose the first **two-dimensional classification framework**, systematically organizing over 60 mainstream transferability metrics along axes of *transferable knowledge type* (e.g., features, relations, semantics) and *measurement granularity* (sample-, task-, or domain-level), while rigorously reconstructing their mathematical foundations, underlying assumptions, and failure boundaries. Through cross-modal and cross-task empirical analysis, we characterize the efficacy gradients and root limitations of metrics across paradigms (e.g., pretraining-finetuning). Our work establishes a standardized assessment pathway and principled metric selection guidelines for transferability evaluation, advancing trustworthy AI evaluation infrastructure, and identifying key future directions—including dynamic transferability modeling and causally grounded metrics.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Computer Vision: Interpretability, Explainability, and Transparency

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• User Modeling, Personalization and Recommendation: Metrics for user behavior and evaluating success

Abstract

Transfer learning has become an essential paradigm in artificial intelligence, enabling the transfer of knowledge from a source task to improve performance on a target task. This approach, particularly through techniques such as pretraining and fine-tuning, has seen significant success in fields like computer vision and natural language processing. However, despite its widespread use, how to reliably assess the transferability of knowledge remains a challenge. Understanding the theoretical underpinnings of each transferability metric is critical for ensuring the success of transfer learning. In this survey, we provide a unified taxonomy of transferability metrics, categorizing them based on transferable knowledge types and measurement granularity. This work examines the various metrics developed to evaluate the potential of source knowledge for transfer learning and their applicability across different learning paradigms emphasizing the need for careful selection of these metrics. By offering insights into how different metrics work under varying conditions, this survey aims to guide researchers and practitioners in selecting the most appropriate metric for specific applications, contributing to more efficient, reliable, and trustworthy AI systems. Finally, we discuss some open challenges in this field and propose future research directions to further advance the application of transferability metrics in trustworthy transfer learning.

Problem

• Assessing knowledge transferability in transfer learning
• Understanding theoretical underpinnings of transferability metrics
• Selecting appropriate metrics for reliable AI systems

Innovation

• Surveying transferability metrics for AI knowledge transfer
• Categorizing metrics by knowledge types and granularity
• Guiding metric selection for trustworthy transfer learning