  Transfer learning methods endeavor to leverage relevant knowledge from
existing source pre-trained models or datasets to solve downstream target
tasks. With the increase in the scale and quantity of available pre-trained
models nowadays, it becomes critical to assess in advance whether they are
suitable for a specific target task. Model transferability estimation is an
emerging and growing area of interest, aiming to propose a metric to quantify
this suitability without training them individually, which is computationally
prohibitive. Despite extensive recent advances already devoted to this area,
they have custom terminological definitions and experimental settings. In this
survey, we present the first review of existing advances in this area and
categorize them into two separate realms: source-free model transferability
estimation and source-dependent model transferability estimation. Each category
is systematically defined, accompanied by a comprehensive taxonomy. Besides, we
address challenges and outline future research directions, intending to provide
a comprehensive guide to aid researchers and practitioners.
