Feature Space Perturbation: A Panacea to Enhanced Transferability Estimation

Prafful Kumar Khoba, Zijian Wang, Chetan Arora, Mahsa Baktashmotlagh; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 1299-1308

Leveraging a transferability estimation metric facilitates the non-trivial challenge of selecting the optimal model for the downstream task from a pool of pre-trained models. Most existing metrics primarily focus on identifying the statistical relationship between feature embeddings and the corresponding labels within the target dataset but overlook crucial aspect of model robustness. This oversight may limit their effectiveness in accurately ranking pre-trained models. To address this limitation we introduce a feature perturbation method that enhances the transferability estimation process by systematically altering the feature space. Our method includes a Spread operation that increases intra-class variability adding complexity within classes and an Attract operation that minimizes the distances between different classes thereby blurring the class boundaries. Through extensive experimentation we demonstrate the efficacy of our feature perturbation method in providing a more precise and robust estimation of model transferability. Notably the existing LogMe method exhibited a significant improvement showing a 28.84% increase in performance after applying our feature perturbation method. The implementation is available at https://github.com/prafful-kumar/enhancing_TE.git