Image classification is one of the most fundamental tasks in Computer Vision.
In practical applications, the datasets are usually not as abundant as those in
the laboratory and simulation, which is always called as Data Hungry. How to
extract the information of data more completely and effectively is very
important. Therefore, an Adaptive Data Augmentation Framework based on the
tensor T-product Operator is proposed in this paper, to triple one image data
to be trained and gain the result from all these three images together with
only less than 0.1% increase in the number of parameters. At the same time,
this framework serves the functions of column image embedding and global
feature intersection, enabling the model to obtain information in not only
spatial but frequency domain, and thus improving the prediction accuracy of the
model. Numerical experiments have been designed for several models, and the
results demonstrate the effectiveness of this adaptive framework. Numerical
experiments show that our data augmentation framework can improve the
performance of original neural network model by 2%, which provides competitive
results to state-of-the-art methods.

本文提出了一种自适应数据增强框架，基于张量 T - 积算子三倍图像数据进行训练并获得结果，可以同时实现列图像嵌入和全局特征交集，模型在空间和频率领域中获取信息，提高了模型的预测精度，通过数值实验，证明了该自适应框架的有效性，完整神经网络模型性能提高了 2％。