## 2 Related Work
### Data Augmentation
#### 2.2.1 Single-Image-Based Data Augmentation (SIBDA)  
Single-image-based data augmentation refers to the data augmentation approaches in which only a single image is required for augmentation. This is the case for traditional augmentation approaches, such as flipping, rotating, etc. These days, SIBDA techniques have advanced from occlusion perspectives, such as random erasing, CutOut, and many more [21]. Closely related SIBDAs are discussed below.  
* **CutOut:** CutOut [34] is data augmentation in which a random part of the image is cut with a square and filled with 0, 255, or the mean of the dataset during training. It was introduced with the purpose of recognizing partially or fully occluded object(s).
* **Random erasing:** Random erasing (RE) [25] is a data augmentation technique in which a rectangular random region of the image is selected at random and erased with a random value with the aim of minimizing overfitting. During training, different occlusion levels are performed not only to improve the performance of the neural network but also to improve the robustness of the model. RE was designed to deal with occlusion in images, thereby forcing the model to learn the erased features. Here, occlusion is where some part(s) of the image is not clear. RE seems similar to CutOut, but the key difference is that RE randomly determines whether to mask out or not, and it determines the aspect ratio and size of the masked region. On the other hand, CutOut does not consider the aspect ratio.
* **Hide and Seek:** Hide and seek (HS) [32] is another data augmentation technique in which the image is divided into squares of the same size as a grid. During the training at each step, a random number of squares is hidden to force the neural network to focus on the most discriminated parts and learn the relevant features. At each epoch, it gives a different view of the image being modeled to learn the important features.  
* **GridMask:** GridMask [33] is a type of data augmentation in which uniform masking is applied to mask out regions in the image and the mask square size changes at each step. Previously discussed data augmentations, such as a CutOut, RE, and HS, randomly erase the region(s) in which there are high chances that either an object is removed or contextual information is lost, which can potentially harm the performance of the model. To trade off between losing contextual information or object removal and performance, GridMask is introduced.  
All of the above SIBDAs have higher chances of losing useful features and, at the same time, are not taking advantage of label smoothing. Label smoothing refers to whatever portion or percentage of the image is erased or mixed; corresponding labels should be mixed in the same ratio. To cater to these features or to information loss, we propose a novel approach named random slices mixing data augmentation (RSMDA). The RSMDA approach is different from SIBDAs because, firstly, it requires two images, and, secondly, it enjoys the benefits of label smoothing. It is logically correct that, whatever portion of the images have mixed, the corresponding labels should be mixed accordingly, as performed in [27; 35; 36].