This paper focuses on model transferability estimation, ie, assessing the performance of pre-
trained models on a downstream task without performing fine-tuning. Motivated by the …

Transfer learning has become a popular method for leveraging pre-trained models in
computer vision. However, without performing computationally expensive fine-tuning, it is …

Intermediate task fine-tuning has been shown to culminate in large transfer gains across
many NLP tasks. With an abundance of candidate datasets as well as pre-trained language …

Transfer learning has become crucial in computer vision tasks due to the vast availability of
pre-trained deep learning models. However, selecting the optimal pre-trained model from a …

Transferability estimation has been an essential tool in selecting a pre-trained model and
the layers in it for transfer learning, to transfer, so as to maximize the performance on a target …

This paper addresses the problem of ranking pre-trained models for object detection and
image classification. Selecting the best pre-trained model by fine-tuning is an expensive and …

Abstract Figuring out which Pre-Trained Model (PTM) from a model zoo fits the target task is
essential to take advantage of plentiful model resources. With the availability of numerous …

This paper addresses an important problem of ranking the pre-trained deep neural networks
and screening the most transferable ones for downstream tasks. It is challenging because …

We address the problem of ensemble selection in transfer learning: Given a large pool of
source models we want to select an ensemble of models which, after fine-tuning on the …

With the increasing abundance of pretrained models in recent years, the problem of
selecting the best pretrained checkpoint for a particular downstream classification task has …