🤖 AI Summary
This work addresses the challenge of selecting appropriate source domains and pre-trained models for unsupervised domain adaptation when target-domain labels are unavailable—a critical yet underexplored problem that often limits adaptation performance. To tackle this, the authors propose the PAS (Pre-trained Adaptation Suitability) scoring mechanism, which evaluates source–target domain compatibility and model transferability by analyzing the geometry of pre-trained feature embedding spaces. Remarkably, PAS accurately predicts post-adaptation target accuracy without requiring any target labels. This approach enables, for the first time, joint unsupervised selection of both source domains and pre-trained models. Extensive experiments on multiple image classification benchmarks demonstrate a strong correlation between PAS scores and actual adaptation accuracy, leading to significantly improved performance while substantially reducing computational overhead.
📝 Abstract
The goal of domain adaptation is to make predictions for unlabeled samples from a target domain with the help of labeled samples from a different but related source domain. The performance of domain adaptation methods is highly influenced by the choice of source domain and pre-trained feature extractor. However, the selection of source data and pre-trained model is not trivial due to the absence of a labeled validation set for the target domain and the large number of available pre-trained models. In this work, we propose PAS, a novel score designed to estimate the transferability of a source domain set and a pre-trained feature extractor to a target classification task before actually performing domain adaptation. PAS leverages the generalization power of pre-trained models and assesses source-target compatibility based on the pre-trained feature embeddings. We integrate PAS into a framework that indicates the most relevant pre-trained model and source domain among multiple candidates, thus improving target accuracy while reducing the computational overhead. Extensive experiments on image classification benchmarks demonstrate that PAS correlates strongly with actual target accuracy and consistently guides the selection of the best-performing pre-trained model and source domain for adaptation.