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LogME

LogME: Practical Assessment of Pre-trained Models for Transfer Learning

How to use

Just feed the features f and labels y to the function, and you can get a nice score which well correlates with the transfer learning performance.

from LogME import LogME
score = LogME(f, y)

Then you can use the score to quickly select a good pre-trained model. The larger the score is, the better transfer performance you get.

Experimental results

We extensively validate the generality and superior performance of LogME on 14 pre-trained models and 17 downstream tasks, covering various pre-trained models (supervised pre-trained and unsupervised pre-trained), downstream tasks (classification and regression), and modalities (vision and language). Check the paper for all the results.

Computer vision

9 datasets and 10 pre-trained models. LogME is a reasonably good indicator for transfer performance.

image-20210222204141915

NLP

7 tasks and 4 pre-trained models. LogME is a good indicator for transfer performance.

image-20210222204350389

Speedup

LogME provides a dramatic speedup for assessing pre-trained models. The speedup comes from two aspects:

  • LogME does not need hyper-parameter tuning whereas vanilla fine-tuning requires extensive hyper-parameter tuning.
  • We designed a fast algorithm to further speedup the computation of LogME.

image-20210222204712553

Citation

If you find it useful, please cite the following paper:

@article{you_logme:_2021,
	title = {LogME: Practical Assessment of Pre-trained Models for Transfer Learning},
	author = {You, Kaichao and Liu, Yong and Long, Mingsheng and Wang, Jianmin},
	journal = {arxiv},
	volume = {abs/2102.11005},
	year = {2021},
	url = {https://arxiv.org/abs/2102.11005},
}

Contact

If you have any question or want to use the code, please contact youkaichao@gmail.com .

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