Big Transfer (BiT): General Visual Representation Learning
Transfer of pre-trained representations improves sample efficiency and simplifies hyperparameter tuning when training 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. 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.
PDF Abstract ECCV 2020 PDF ECCV 2020 AbstractCode
Colab
CIFAR-10
ImageNet
MS COCO
CIFAR-100
Oxford 102 Flower
ObjectNet
JFT-300M
OmniBenchmark
ImageNet-W
Results from the Paper
Ranked #1 on
Out-of-Distribution Generalization
on ImageNet-W
(using extra training data)
