“Big Transfer (BiT): General Visual Representation Learning”, 2019-12-24 (; similar):
Transfer of pre-trained representations improves sample efficiency and simplifies hyperparameter tuning when deep neural networks for vision. We revisit the paradigm of pre-training on large supervised datasets and fine-tuning the model on a target task. We scale up pre-training, and propose a simple recipe that we call Big Transfer (BiT).
By combining a few carefully selected components, and transferring using a simple heuristic, we achieve strong performance on over 20 datasets. BiT performs well across a surprisingly wide range of data regimes—from 1 example per class to 1M total examples [using JFT-300M]…Note that exclusively using more data or larger models may hurt performance; instead, both need to be increased in tandem.
BiT achieves 87.5% top-1 accuracy on ILSVRC-2012, 99.4% on CIFAR-10, and 76.3% on the 19 task Visual Task Adaptation Benchmark (VTAB). On small datasets, BiT attains 76.8% on ILSVRC-2012 with 10 examples per class, and 97.0% on CIFAR-10 with 10 examples per class.
We conduct detailed analysis of the main components that lead to high transfer performance.