Pre-Trained Model Recommendation for Downstream Fine-tuning

arXiv:2403.06382v1 Announce Type: cross
Abstract: As a fundamental problem in transfer learning, model selection aims to rank off-the-shelf pre-trained models and select the most suitable one for the new target task. Existing model selection techniques are often constrained in their scope and tend to overlook the nuanced relationships between models and tasks. In this paper, we present a pragmatic framework \textbf{Fennec}, delving into a diverse, large-scale model repository while meticulously considering the intricate connections between tasks and models. The key …

abstract arxiv cs.ai cs.cv cs.lg fine-tuning model selection pre-trained models recommendation relationships tasks transfer transfer learning type