核心概念
Kernel Normalization (KernelNorm) and kernel normalized convolutional layers offer a solution to the limitations of BatchNorm, providing higher performance and efficiency in deep CNNs.
要約
The study introduces Kernel Normalization (KernelNorm) as an alternative to BatchNorm in convolutional neural networks. By incorporating KernelNorm and KNConv layers, KNConvNets achieve superior performance compared to BatchNorm counterparts. The overlapping normalization units of KernelNorm allow for better utilization of spatial correlation during normalization, leading to faster convergence rates and improved accuracy. Experimental results demonstrate the effectiveness of KNResNets in image classification and semantic segmentation tasks across various datasets.
The research compares the performance of KNResNets with BatchNorm, GroupNorm, LayerNorm, and LocalContextNorm counterparts. KNResNets consistently outperform the competitors in terms of accuracy, convergence rate, and generalizability. Additionally, the study explores the computational efficiency and memory usage of KNResNets compared to batch normalized models.
Furthermore, the study highlights the potential applications of KernelNormalization beyond ResNets, showcasing its effectiveness in architectures like ConvNext. Future work may focus on optimizing the implementation of KNResNets for even greater efficiency and scalability.
統計
Existing convolutional neural network architectures rely upon batch normalization (BatchNorm) for effective training.
Kernel normalization (KernelNorm) offers an alternative to BatchNorm by addressing limitations with small batch sizes.
KNConvNets achieve higher or competitive performance compared to BatchNorm counterparts in image classification and semantic segmentation.