In this paper, we present augmentation inside the network, a method that simulates data augmentation techniques for computer vision problems on intermediate features of a convolutional neural network. We perform these transformations, changing the data flow through the network, and sharing common computations when it is possible. Our method allows us to obtain smoother speed-accuracy trade-off adjustment and achieves better results than using standard test-time augmentation (TTA) techniques. Additionally, our approach can improve model performance even further when coupled with test-time augmentation. We validate our method on the ImageNet-2012 and CIFAR-100 datasets for image classification. We propose a modification that is 30% faster than the flip test-time augmentation and achieves the same results for CIFAR-100.
@article{arxiv.2012.10769,
title = {Augmentation Inside the Network},
author = {Maciej Sypetkowski and Jakub Jasiulewicz and Zbigniew Wojna},
journal= {arXiv preprint arXiv:2012.10769},
year = {2023}
}