Fixing the train-test resolution discrepancy
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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 on 128x128 images, and 78 Conversely, when training a PNASNet at resolution 331x331 and further optimizing for test resolution 480x480, we obtain a test top-1 accuracy of 83.7
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