We thank the reviewers for their constructive feedback on the paper. Here we answer their main questions and comments. In addition, are the results shown significant? In particular, we have evaluated our approach for transfer learning for low-resource and/or fine-grained classification. Then (3) we use our method, i.e. we fine-tune the last Finally, we applied our method to a very large ResNeXt-101 32x48d from [Mahajan et al.

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 size of the objects seen by the classifier at train and test time: in fact, a lower train resolution improves the classification at test time! We then propose a simple strategy to optimize the classifier performance, that employs different train and test resolutions. It relies on a computationally cheap fine-tuning of the network at the test resolution. This enables training strong classifiers using small training images, and therefore significantly reduce the training time. 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 at 224x224. A ResNeXt-101 32x48d pre-trained with weak supervision on 940 million 224x224 images and further optimized with our technique for test resolution 320x320 achieves 86.4% top-1 accuracy (top-5: 98.0%). To the best of our knowledge this is the highest ImageNet single-crop accuracy to date.

Clarity: The paper is clearly written and easy to follow. Significance: The results in the paper are significant for the practitioners and existing deployments as they shed light on the train-test resolution discrepancy and suggest method to improve test performance for existing trained models. Novelty: The analysis in this paper is novel (though improved performance on higher resolution images has been observed earlier). Questions: While the focus is on fixing discrepancy after the model has been initially trained, why not just fix the training such that there is no discrepancy, as opposed to changing the size for test and finetuning? Line 110-111 derives f sqrt(HW), which does not seem to be right since k doesn't include the sensor size.

I think the paper addresses an interesting problem, albeit limited in scope to computer vision. I am sure practitioners in that field will appreciate the paper's findings. Two of the reviewers were positive, and reaffirmed their position during the post-rebuttal discussion, while R1 remained concerned, in particular regarding lack of rigorous statistical analysis of the results. The other reviewers did not consider that issue a deal-breaker, and I agree and recommend to accept.

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 size of the objects seen by the classifier at train and test time: in fact, a lower train resolution improves the classification at test time! We then propose a simple strategy to optimize the classifier performance, that employs different train and test resolutions. It relies on a computationally cheap fine-tuning of the network at the test resolution. This enables training strong classifiers using small training images, and therefore significantly reduce the training time. 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 at 224x224.

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 size of the objects seen by the classifier at train and test time: in fact, a lower train resolution improves the classification at test time! We then propose a simple strategy to optimize the classifier performance, that employs different train and test resolutions. It relies on a computationally cheap fine-tuning of the network at the test resolution. This enables training strong classifiers using small training images, and therefore significantly reduce the training time.