Random erasing selects a rectangle region in an image and replaces or overlays it with a gray, black, white, or Gaussian noise pixels rectangle. It is counterintuitive to why this technique increases the AI model’s forecasting accuracy.
The strength of any ML model, especially CNN, is in predicting or forecasting data that has not been seen in the training or validating stage. Thus, dropout, where randomly selected neurons are ignored during training, is a well-proven method to reduce overfitting and increase accuracy. Therefore, random erasing has the same effect as increasing the dropout rate.
A paper called Random Erasing Data Augmentation, which was published on November 16, 2017, by arXiv, shows how random erasing increases accuracy and reduces overfitting in a CNN-based model. The paper’s authors are Zhun Zhong, Liang Zheng, Guoliang Kang, Shaozi Li, and Yi Yang from the Cognitive Science Department, at Xiamen University, China, and the University...