ETran: Energy-Based Transferability Estimation
Mohsen Gholami Mohammad Akbari Xinglu Wang Behnam Kamranian Yong Zhang
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 time-consuming task. Previous works have
proposed transferability estimation based on features extracted by the
pre-trained models. We argue that quantifying whether the target dataset is
in-distribution (IND) or out-of-distribution (OOD) for the pre-trained model is
an important factor in the transferability estimation. To this end, we propose
ETran, an energy-based transferability assessment metric, which includes three
scores: 1) energy score, 2) classification score, and 3) regression score. We
use energy-based models to determine whether the target dataset is OOD or IND
for the pre-trained model. In contrast to the prior works, ETran is applicable
to a wide range of tasks including classification, regression, and object
detection (classification+regression). This is the first work that proposes
transferability estimation for object detection task. Our extensive experiments
on four benchmarks and two tasks show that ETran outperforms previous works on
object detection and classification benchmarks by an average of 21% and 12%,
respectively, and achieves SOTA in transferability assessment.