T-ADAF: Adaptive Data Augmentation Framework for Image Classification Network based on Tensor T-product Operator
This addresses data hungry issues in computer vision for practical applications, but it is incremental as it builds on existing data augmentation methods.
The paper tackles the problem of data scarcity in image classification by proposing T-ADAF, an adaptive data augmentation framework based on the tensor T-product operator, which triples image data for training with minimal parameter increase and improves model performance by 2%.
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.