Abstract
In the evolving landscape of deep learning, selecting the best pre-trained models from a growing number of choices is a challenge. While fine-tuning each pre-trained model and comparing their final accuracy can provide precise rankings for a new task, this greedy approach is often prohibitive due to the substantial computing resources required. Transferability estimation literature propos…