  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.
