Transferability Estimation Based On Principal Gradient Expectation
This addresses the challenge of efficiently choosing source domains for transfer learning in machine learning applications, though it appears incremental as it builds on existing transferability estimation methods.
The paper tackles the problem of selecting a source domain for transfer learning by proposing Principal Gradient Expectation (PGE), a method for estimating transferability that balances performance and cost, and shows it outperforms state-of-the-art methods in stability, reliability, and efficiency.
Transfer learning aims to improve the performance of target tasks by transferring knowledge acquired in source tasks. The standard approach is pre-training followed by fine-tuning or linear probing. Especially, selecting a proper source domain for a specific target domain under predefined tasks is crucial for improving efficiency and effectiveness. It is conventional to solve this problem via estimating transferability. However, existing methods can not reach a trade-off between performance and cost. To comprehensively evaluate estimation methods, we summarize three properties: stability, reliability and efficiency. Building upon them, we propose Principal Gradient Expectation(PGE), a simple yet effective method for assessing transferability. Specifically, we calculate the gradient over each weight unit multiple times with a restart scheme, and then we compute the expectation of all gradients. Finally, the transferability between the source and target is estimated by computing the gap of normalized principal gradients. Extensive experiments show that the proposed metric is superior to state-of-the-art methods on all properties.