Frustratingly Easy Transferability Estimation

• URL: http://arxiv.org/abs/2106.09362v1
• Date: Thu, 17 Jun 2021 10:27:52 GMT
• Title: Frustratingly Easy Transferability Estimation
• Authors: Long-Kai Huang, Ying Wei, Yu Rong, Qiang Yang and Junzhou Huang
• Abstract summary: We propose a simple, efficient, and effective transferability measure named TransRate. TransRate measures the transferability as the mutual information between the features of target examples extracted by a pre-trained model and labels of them. Despite its extraordinary simplicity in 10 lines of codes, TransRate performs remarkably well in extensive evaluations on 22 pre-trained models and 16 downstream tasks.
• Score: 64.42879325144439
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Transferability estimation has been an essential tool in selecting a pre-trained model and the layers of it to transfer, so as to maximize the performance on a target task and prevent negative transfer. Existing estimation algorithms either require intensive training on target tasks or have difficulties in evaluating the transferability between layers. We propose a simple, efficient, and effective transferability measure named TransRate. With single pass through the target data, TransRate measures the transferability as the mutual information between the features of target examples extracted by a pre-trained model and labels of them. We overcome the challenge of efficient mutual information estimation by resorting to coding rate that serves as an effective alternative to entropy. TransRate is theoretically analyzed to be closely related to the performance after transfer learning. Despite its extraordinary simplicity in 10 lines of codes, TransRate performs remarkably well in extensive evaluations on 22 pre-trained models and 16 downstream tasks.

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