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Poster
Fixing the train-test resolution discrepancy
Hugo Touvron · Andrea Vedaldi · Matthijs Douze · Herve Jegou

Thu Dec 12 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #141
in Deep Learning -- CNN Architectures »

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.

Keywords: Supervised Deep Networks · Computer Vision · Applications · Algorithms -> Classification; Deep Learning; Deep Learning
[ Paper]  [ Poster] 

Author Information

Hugo Touvron (Facebook AI Research)
Andrea Vedaldi (Facebook AI Research and University of Oxford)
Matthijs Douze (Facebook AI Research)
Herve Jegou (Facebook AI Research)

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