论文标题
使用频率选择性网格重新采样来提高神经网络的准确性
Increasing the Accuracy of a Neural Network Using Frequency Selective Mesh-to-Grid Resampling
论文作者
论文摘要
神经网络几乎被广泛用于识别图像内容的任何任务。尽管已经为研究有效的网络架构，优化器和培训策略而付出了很多努力，但图像插值对神经网络性能的影响尚未得到很好的研究。此外，研究表明，神经网络通常对输入图像的微小变化敏感，从而导致其性能急剧下降。因此，我们建议在本文中使用关键点不可知频率选择性网格到网格重采样（FSMR）来处理神经网络的输入数据。这种基于模型的插值方法已经表明，它能够在PSNR方面胜过公共插值方法。我们使用广泛的实验评估表明，根据网络体系结构和分类任务，FSMR在培训过程中的应用有助于学习过程。此外，我们表明在应用阶段使用FSMR是有益的。对于RESNET50和OxFlower17数据集，可以提高分类精度高达4.31个百分点。
Neural networks are widely used for almost any task of recognizing image content. Even though much effort has been put into investigating efficient network architectures, optimizers, and training strategies, the influence of image interpolation on the performance of neural networks is not well studied. Furthermore, research has shown that neural networks are often sensitive to minor changes in the input image leading to drastic drops of their performance. Therefore, we propose the use of keypoint agnostic frequency selective mesh-to-grid resampling (FSMR) for the processing of input data for neural networks in this paper. This model-based interpolation method already showed that it is capable of outperforming common interpolation methods in terms of PSNR. Using an extensive experimental evaluation we show that depending on the network architecture and classification task the application of FSMR during training aids the learning process. Furthermore, we show that the usage of FSMR in the application phase is beneficial. The classification accuracy can be increased by up to 4.31 percentage points for ResNet50 and the Oxflower17 dataset.
