## 1 Introduction  
Deep learning (DL) has shown a significant performance gain in various domains, including image classification [1; 2; 3; 4; 5; 6; 7; 8; 9; 10], audio classification [11; 12; 13; 14; 15; 16; 17], and text classification [18; 19; 20]. This performance gain is due to three major factors [21]: (i) deep neural network architectures' progress, (ii) high computational power, and (iii) access to large-scale data [21]. Among these factors, research on data has been the hot topic considering that DL convolutional neural networks (CNNs) require a huge amount of data. The data is required for better generalization, which, in turn, can prevent and reduce overfitting. Overfitting occurs when the network performance on training data becomes very high, and the network performance on unseen (validation) data worsens; alternatively, overfitting can be observed when the training error is low but the unseen (validation) error is high. There are two major categories of approaches to prevent overfitting. The first is model regularization [22], which is a technique that selects the desired model's complexity level, or the level at which the model provides better generalization. Examples of this class are batch normalization [23] and dropout [24]. The second category is data augmentation [25; 26; 27; 21], which uses or re-mixes existing data to create new and more diverse training data. There are different data augmentations (DAs), divided into two categories; (i) traditional data augmentations such as flipping [21; 25], cropping [21; 25], resizing [25], and many more [21] and (ii) advanced data augmentation. Recent research has demonstrated that traditional data augmentations do not provide enough diversity for the highly parameterized CNN architectures, and, as a result, they can overfit easily[27; 28; 29; 30; 31]. Thus, advanced data augmentations have ignited the interest of the research community. Examples of advanced data augmentations are random erasing (RE) [25], hide and seek (HS) [32], GridMask [33], CutOut [34], MixUp [35], CutMix [36], RICAP [27], mixed example data augmentations [37], and many more [21]. Single-image deletion has been studied by many researchers with techniques such as RE, HS, GridMask, and CutOut. Furthermore, multi-image mixing techniques have been studied, such as CutMix, MixUp, RICAP, and many others [21]. Single-image deletion and multi-image mixing techniques are illustrated in Figure 1. Single-image deletion techniques may lose features, consequently deteriorating the performance of DL models, and multi-image mixing techniques have explored augmentations from different perspectives, but none have considered data augmentation based on slice mixing. To the best of our knowledge, we are the first to explore random slices mixing to check the effect of the proposed data augmentation technique using different strategies, namely, the horizontal (row-wise) slices mixing strategy, the vertical (column-wise) slices mixing strategy, and a mixture of both, as shown in Figure 2. The research question we address is: _can the proposed data augmentation technique preserve the features' information lost in single-image data augmentations?_ The proposed technique obtains slices of one image and places them onto another image to create a third image and enrich the variety of training images available. In our previous work [38], we introduced only horizontal slice mixing and validated the approach for the limited models without finding the optimal hyperparameters, such as the probability and slice size. In this work, we are extending the previous work [38] to vertical slice mixing and a mixture of both, and are using numerous bigger models in addition to finding the optimal hyperparameters. Furthermore, robustness against adversarial attacks is checked, and RSMDA class activation maps are compared with other SOTA methods to visualize the focus of the models. In the remainder of the paper, the terms \"network\" and \"model\" are used interchangeably.