Data enhancement method based on attention activation map
Jian Guo, Quansheng Dou · 2023
Overfitting is a major problem faced by deep learning, and data enhancement is an effective means to mitigate it. Erasure-based data enhancement improves model generalization by generating new training samples, but these methods lead to over-erasure and under-erasure problems due to the random nature of erasure. To alleviate these problems, this paper proposes a new attention activation map-based image erasure method: Aag-dem, selectively erase the relevant regions of the image to generate high-quality training data and finally a large number of experiments are done to prove the effectiveness and generality of the method, and the performance of the baseline is improved to some extent.