  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.
