Quantifying Dataset Similarity to Guide Transfer Learning

Transfer learning has become a cornerstone of modern machine learning, as it can empower models by leveraging knowledge from related domains to improve learning effectiveness. However, transferring from poorly aligned data can harm rather than help performance, making it crucial to determine whether the transfer will be beneficial before implementation. This work aims to address this challenge by proposing an innovative metric to measure dataset similarity and provide quantitative guidance on transferability. In the literature, existing methods largely focus on feature distributions while overlooking label information and predictive relationships, potentially missing critical transferability insights. In contrast, our proposed metric, the Cross-Learning Score (CLS), measures dataset similarity through bidirectional generalization performance between domains. We provide a theoretical justification for CLS by establishing its connection to the cosine similarity between the decision boundaries for the target and source datasets. Computationally, CLS is efficient and fast to compute as it bypasses the problem of expensive distribution estimation for high-dimensional problems. We further introduce a general framework that categorizes source datasets into positive, ambiguous, or negative transfer zones based on their CLS relative to the baseline error, enabling informed decisions. Additionally, we extend this approach to encoder-head architectures in deep learning to better reflect modern transfer pipelines. Extensive experiments on diverse synthetic and real-world tasks demonstrate that CLS can reliably predict whether transfer will improve or degrade performance, offering a principled tool for guiding data selection in transfer learning.