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.

翻译：本文提出了一种名为“网络内部增强”的方法，该方法在卷积神经网络的中间特征上模拟了针对计算机视觉问题的数据增强技术。我们执行这些变换，改变通过网络的数据流，并在可能时共享公共计算。该方法使我们能够获得更平滑的速度-精度权衡调整，并比使用标准测试时增强（TTA）技术取得更好的结果。此外，我们的方法在与测试时增强结合使用时，能进一步提升模型性能。我们在ImageNet-2012和CIFAR-100数据集上对图像分类任务进行了方法验证。我们提出了一种改进方案，该方案比翻转测试时增强快30%，并在CIFAR-100上取得了相同的结果。