论文标题
SuperPixelGridCut，SuperpixelGridMean和SuperpixelGridMix数据增强
SuperpixelGridCut, SuperpixelGridMean and SuperpixelGridMix Data Augmentation
论文作者
论文摘要
提出了一种基于不规则超像素分解的新型数据增强方法。这种方法称SuperPixelGridMasks允许扩展原始图像数据集，这些图像数据集是通过训练与机器学习相关的分析体系结构来提高性能所需的。提出了三种名为SuperPixelGridCut，SuperpixelGridMean和SuperPixelGridMix的变体。这些基于网格的方法使用信息的下降和融合产生了新的图像转换样式。使用各种图像分类模型和数据集的广泛实验表明，使用我们的方法可以显着胜过基线性能。比较研究还表明，我们的方法可以超过其他数据增强的性能。超出不同本质的图像识别数据集获得的实验结果显示了这些新方法的效率。 https://github.com/hammoudiproject/superpixelgridmasks公开获得SuperPixelGridCut，SuperPixelGridMean和SuperPixelGridMix代码
A novel approach of data augmentation based on irregular superpixel decomposition is proposed. This approach called SuperpixelGridMasks permits to extend original image datasets that are required by training stages of machine learning-related analysis architectures towards increasing their performances. Three variants named SuperpixelGridCut, SuperpixelGridMean and SuperpixelGridMix are presented. These grid-based methods produce a new style of image transformations using the dropping and fusing of information. Extensive experiments using various image classification models and datasets show that baseline performances can be significantly outperformed using our methods. The comparative study also shows that our methods can overpass the performances of other data augmentations. Experimental results obtained over image recognition datasets of varied natures show the efficiency of these new methods. SuperpixelGridCut, SuperpixelGridMean and SuperpixelGridMix codes are publicly available at https://github.com/hammoudiproject/SuperpixelGridMasks
