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.
Abstract (translated)
迁移学习已经成为人工智能领域的一个重要范式，它通过将知识从源任务转移到目标任务来提升性能。这种方法在计算机视觉和自然语言处理等领域中，尤其是在预训练和微调技术的帮助下取得了显著成功。然而，尽管迁移学习得到了广泛应用，如何可靠地评估知识的可转移性仍然是一项挑战。理解每种可转移性度量的理论基础对于确保迁移学习的成功至关重要。在这项调查中，我们提供了一个统一的可转移性度量分类体系，基于可转移的知识类型和测量粒度进行分类。这项工作审查了各种用于评估源知识在迁移学习中的潜力以及这些度量在不同学习范式中的适用性的指标，并强调了谨慎选择这些度量的重要性。 通过揭示这些不同的度量在不同条件下的工作机制，本调查旨在指导研究人员和实践者为特定应用选择最合适的度量标准，从而促进更高效、可靠且值得信赖的人工智能系统的开发。最后，我们讨论了一些开放性挑战并提出未来研究方向，以进一步推进可转移性度量在可信迁移学习中的应用。
URL
https://arxiv.org/abs/2507.03175
