论文标题
一种新的数据增强方法：九个点移动最小平方（ND-ML）
A novel method for data augmentation: Nine Dot Moving Least Square (ND-MLS)
论文作者
论文摘要
数据增强大大增加了基于标记的数据获得的数据量，以节省数据收集和标签的费用和人工。我们提出了一种新的数据增强方法，称为九点MLS（ND-MLS）。基于图像Defor-Mation的概念提出了这种方法。图像是根据控制点的变形，该控制点由ND-ML计算。该方法可以在短时间内为一个存在的数据集生成2000多个图像。为了验证此数据增强方法，进行了广泛的测试，涵盖了计算机视觉的3个主要任务，即分类，检测和分割。结果表明，在分类中，每类使用10张图像进行培训，VGGNET可以通过ND-MLS在手写数字的MNIST数据集上获得92％的Top-1 ACC。在Omniglot数据集中，随着字符类别的增加，少数弹出的精度通常会降低。但是，ND-MLS方法具有稳定的性能，并在100个不同的手写字符分类任务上在RES-NET中获得96.5 TOP-1 ACC； 2）在细分中，在仅十个原始图像的前提下，DeepLab在瓶子，马和草测试数据集中分别获得了93.5％，85％和73.3％的M_IOU（10），而CAT测试数据集则获得86.7％的M_IOU（10）M_IOU（10）。 3）Yolo V4只有10个类别的原始图像，分别获得100％和97.2％的瓶子和马匹检测，而CAT数据集则使用Yolo V3获得93.6％。总而言之，ND-ML只能通过仅使用几个数据来在分类，对象检测和语义分割任务上表现良好。
Data augmentation greatly increases the amount of data obtained based on labeled data to save on expenses and labor for data collection and labeling. We present a new approach for data augmentation called nine-dot MLS (ND-MLS). This approach is proposed based on the idea of image defor-mation. Images are deformed based on control points, which are calculated by ND-MLS. The method can generate over 2000 images for one exist-ing dataset in a short time. To verify this data augmentation method, extensive tests were performed covering 3 main tasks of computer vision, namely, classification, detection and segmentation. The results show that 1) in classification, 10 images per category were used for training, and VGGNet can obtain 92% top-1 acc on the MNIST dataset of handwritten digits by ND-MLS. In the Omniglot dataset, the few-shot accuracy usu-ally decreases with the increase in character categories. However, the ND-MLS method has stable performance and obtains 96.5 top-1 acc in Res-Net on 100 different handwritten character classification tasks; 2) in segmentation, under the premise of only ten original images, DeepLab obtains 93.5%, 85%, and 73.3% m_IOU(10) on the bottle, horse, and grass test datasets, respectively, while the cat test dataset obtains 86.7% m_IOU(10) with the SegNet model; 3) with only 10 original images from each category in object detection, YOLO v4 obtains 100% and 97.2% bottle and horse detection, respectively, while the cat dataset obtains 93.6% with YOLO v3. In summary, ND-MLS can perform well on classification, object detec-tion, and semantic segmentation tasks by using only a few data.
