Augmentation Inside the Network

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 co...

Full description

Saved in:
Bibliographic Details
Published inarXiv.org
Main Authors Sypetkowski, Maciej, Jasiulewicz, Jakub, Wojna, Zbigniew
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 23.06.2023
Subjects
Artificial neural networks
Computer Science - Computer Vision and Pattern Recognition
Computer Science - Learning
Computer Science - Neural and Evolutionary Computing
Computer vision
Data augmentation
Image classification
Testing time
Online AccessGet full text

Cover

More Information
Summary: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.
ISSN:2331-8422
DOI:10.48550/arxiv.2012.10769