Normalization is a pre-processing step that converts the data into a more usable representation. As part of the deep neural networks (DNNs), the batch normalization (BN) technique uses normalization to address the problem of internal covariate shift. It can be packaged as general modules, which have been extensively integrated into various DNNs, to stabilize and accelerate training, presumably leading to improved generalization. However, the effect of BN is dependent on the mini-batch size and it does not take into account any groups or clusters that may exist in the dataset when estimating population statistics. This study proposes a new normalization technique, called context normalization, for image data. This approach adjusts the scaling of features based on the characteristics of each sample, which improves the model's convergence speed and performance by adapting the data values to the context of the target task. The effectiveness of context normalization is demonstrated on various datasets, and its performance is compared to other standard normalization techniques.
翻译:正常化是将数据转换为更实用的表示法的预处理步骤。作为深神经网络的一部分,批量正常化技术(BN)使用正常化方法来解决内部共变转移的问题,可以作为一般模块包装,这些模块已广泛纳入各种DNN, 以稳定和加速培训, 可能会导致改进一般化。但是, BN的效果取决于微型批量大小,在估计人口统计时没有考虑到数据集中可能存在的任何组或组。本研究提出了一种新的正常化技术,称为环境正常化,用于图像数据。这种方法根据每个样本的特性调整特征,通过调整数据值以适应目标任务的背景来提高模型的趋同速度和性能。背景正常化的效果在各种数据集中得到证明,其性能与其他标准正常化技术进行比较。</s>