Augmentation Inside the Network

• URL: http://arxiv.org/abs/2012.10769v2
• Date: Fri, 23 Jun 2023 18:37:27 GMT
• Title: Augmentation Inside the Network
• Authors: Maciej Sypetkowski, Jakub Jasiulewicz, Zbigniew Wojna
• Abstract summary: We present augmentation inside the network, a method that simulates data augmentation techniques for computer vision problems. We validate our method on the ImageNet-2012 and CIFAR-100 datasets for image classification.
• Score: 1.5260179407438161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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