LogME: Practical Assessment of Pre-trained Models for Transfer Learning
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This paper studies task adaptive pre-trained model selection, an underexplored problem of assessing pre-trained models so that models suitable for the task can be selected from the model zoo without fine-tuning. A pilot work <cit.> addressed the problem in transferring supervised pre-trained models to classification tasks, but it cannot handle emerging unsupervised pre-trained models or regression tasks. In pursuit of a practical assessment method, we propose to estimate the maximum evidence (marginalized likelihood) of labels given features extracted by pre-trained models. The maximum evidence is less prone to over-fitting than the likelihood, and its expensive computation can be dramatically reduced by our carefully designed algorithm. The Logarithm of Maximum Evidence (LogME) can be used to assess pre-trained models for transfer learning: a pre-trained model with high LogME is likely to have good transfer performance. LogME is fast, accurate, and general, characterizing it as the first practical assessment method for transfer learning. Compared to brute-force fine-tuning, LogME brings over 3000× speedup in wall-clock time. It outperforms prior methods by a large margin in their setting and is applicable to new settings that prior methods cannot deal with. It is general enough to diverse pre-trained models (supervised pre-trained and unsupervised pre-trained), downstream tasks (classification and regression), and modalities (vision and language). Code is at <https://github.com/thuml/LogME>.
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