## 2 Related Work
### Data Augmentation
#### 2.2.2 Multi-Image-Based Data Augmentation  
Multi-image-based data augmentation (MIBDA) refers to the data augmentation category where it requires more than one image to create the augmented image. Several methods exist for MIBDA, such as MixUp [35], CutMix [36], RICAP [27], mixed example data augmentations [37], and many more [21]. We have limited the MIBDA techniques' literature review so that is closely related to the proposed technique.  
* **MixUp:** MixUp [35] is a data augmentation that creates a new augmented image using a weighted combination of two images, and label smoothing is performed simultaneously. It demonstrated impressive performance on a variety of tasks.
* **CutMix:** CutMix [36], a data augmentation method, is introduced to deal with the problems of information loss and the inefficiency of regional dropout methods. In CutMix, a random region of one image is replaced with a patch from another image, and the corresponding labels are mixed as well. It demonstrates high regularization in a wide range of tasks. This method uses only two images.
* **Random Image Cropping and Patching Data Augmentation for Deep CNNs (RICAP)**: RICAP [27] is another data augmentation technique that is similar to CutMix except that it uses four images. RICAP further increases the diversity of the training data, enabling it to learn more features; it has shown good performance. More importantly, the labels of the four images are also mixed.
* **Improved Mixed Example Data Augmentation**: Improved mixed example data augmentation (IMEDA) [37] is another data augmentation that explores augmentation to check the importance of linearity using non-label preserving data augmentations in its search space. IMEDA explores the number of data augmentations and shows a massive performance gain over the SOTA methods.  
All of the MIBDA techniques have explored data augmentation from different mixing points of view, which is closely relevant to RSMDA. The key difference between the previous works and RSMDA is that none of these previous data augmentations individually explored data augmentation from a slice mixing point of view. To the best of our knowledge, we are the first to explore data augmentation from a slice mixing point of view in detail. We make the following contributions to this work.  
* We propose a novel data augmentation technique named random slices mixing data augmentation (RSMDA).
* We propose and investigate three different RSMDA strategies, namely, horizontal (row-wise) slice mixing, vertical (column-wise) slice mixing, and a combination of both.  
* We validate this approach using different models' architectures across different datasets. RSMDA is not only effective in its accuracy performance but also robust against adversarial attacks.
* We investigate the RSMDA hyperparameters in detail, provide analysis, and compare them with SOTA augmentations using class activation maps (CAMs).
* Finally, we provide the full source code for RSMDA in an open repository: [[https://github.com/kmr2017/Slices-aug](https://github.com/kmr2017/Slices-aug)]([https://github.com/kmr2017/Slices-aug](https://github.com/kmr2017/Slices-aug)) (accessed on 8 December 2022).