Positional normalization-based mixed-image data augmentation and ensemble self-distillation algorithm

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The prior approach to data augmentation using positional normalization necessitated manual determination of weight parameters for blending two images. However, theoretically, these parameters cannot cater to all samples. We propose a novel data augmentation approach called pnMix (Positional normalization-based data mixing) based on data blending and positional normalization to address these issues. Moreover, we have improved the teacher model of an existing network structure based on self-distillation technology using an ensembling approach called ESKD. Our method enhances performance across multiple datasets and tasks. We outperform previous methods in complex real-world datasets such as imagenet-a and imagenet-o, which we believe are highly meaningful for practical applications. Specifically, we obtain good results on the CIFAR100 and Imagenet image classification datasets, the Pascal VOC and COCO object detection datasets, and the COCO and Cityscapes image segmentation datasets. Furthermore, we analyze the pnMix method's advantages compared to other data augmentation methods through visualization techniques, demonstrating its effectiveness and interpretability. Our source code and pre-trained models are available at https://github.com/sydney72380/pnMix.