Data-augmentation is key to the training of neural networks for image
classification. This paper first shows that existing augmentations induce a
significant discrepancy between the typical size of the objects seen by the
classifier at train and test time. We experimentally validate that, for a
target test resolution, using a lower train resolution offers better
classification at test time.
We then propose a simple yet effective and efficient strategy to optimize the
classifier performance when the train and test resolutions differ. It involves
only a computationally cheap fine-tuning of the network at the test resolution.
This enables training strong classifiers using small training images. For
instance, we obtain 77.1% top-1 accuracy on ImageNet with a ResNet-50 trained
on 128x128 images, and 79.8% with one trained on 224x224 image. In addition, if
we use extra training data we get 82.5% with the ResNet-50 train with 224x224
images.
Conversely, when training a ResNeXt-101 32x48d pre-trained in
weakly-supervised fashion on 940 million public images at resolution 224x224
and further optimizing for test resolution 320x320, we obtain a test top-1
accuracy of 86.4% (top-5: 98.0%) (single-crop). To the best of our knowledge
this is the highest ImageNet single-crop, top-1 and top-5 accuracy to date.

本文研究了如何通过使用低分辨率图像进行数据增广和微调来提高神经网络在图像分类中的性能，特别是在训练和测试分辨率不同时。通过实验证明，这种方法可以提高分类器的准确率，在 ImageNet 数据集上的单张图片分类准确率达到 86.4%（top-5:98.0%）。