## 1 Introduction
### Data Augmentation  
Resampling techniques in traditional statistics have had great success in providing more robust methods to test statistical approximations and \(p\)-value calculations. Tests requiring permutations as well as more computationally efficient Monte Carlo simulations have been a standard procedure in statistical practices for over half a century [36; 37]. Their versatility to both parametric and non-parametric assumptions makes handling imbalanced and skewed data much more reliable, while proving less sensitive to samples of smaller sizes [38]. Nevertheless, a critical issue when considering small sample sizes are an \"insufficiency of information density\" that is able to correctly provide a general overview of the population's distribution [39]. This issue becomes apparent when trying to classify new individuals. With insufficient knowledge of the true coverage of a domain, the interpretation of new information is  
Figure 1: Examples of landmark types on different artefacts in the fossil register. Type I landmarks (black) refer to points of biological and anatomical interest, such as the meeting of two sutures or a foramen as represented on the skull above. Type II landmarks (red) are mathematically defined points of interest, such as those points marking the maximal curvature of one, the length of an item, or the deepest point in a microscopic groove. Type III landmarks (blue) are constructed points of interest located in approximation or relation with other elements, such as the centroid of an eye-socket, the general outline of an object, or points in between other landmark types. much more difficult. In data science this phenomenon is usually known as _overfitting_ for classification algorithms [25]. One statistical technique frequently used to overcome this issue is resampling with replacement, known as _bootstrapping_. Bootstrapping duplicates the data multiple times creating a virtual population from a distribution sample [40; 41]. As opposed to resampling techniques without replacement (e.g., permutation, cross-validation, jackknife), bootstrap procedures are efficient in inferential tasks helping to simulate the general nature of the population. Nevertheless, neither of these resampling procedures, in truth, simulate new information. While they may be useful for inflating the dataset and providing enough information for a model to adjust its weights, overfitting is likely, as the space between data points can still be considered \"uncharted territory\". In response, data scientists and specialists in AIAs propose the use of synthetically produced new data to overcome these problems [42]. While using synthetic \"fake\" data has drawn some skepticism from scientists, numerous experiments in predictive modelling have empirically shown how these synthetic datasets not only reduce overfitting, but actually produce an increase in accuracy [43]. This is achieved through creating new data that is \"meaningful\" to the real distribution by adapting the data that is already available [44] ([PERSON] and [PERSON], 2019). These advances have had a major impact on scientific disciplines dedicated to computational learning, especially in the case of highly complex applications for computer vision [25]. One of the key AIAs responsible for this success is the Generative Adversarial Network (GAN). GANs were originally presented as an unsupervised AIA capable of creating new data, based on the training data provided [45]. In less than a decade, GANs have been efficiently incorporated into a wide variety of applications, especially in fields of computer vision and image processing. A GAN consists of two neural networks trained simultaneously. The first model, known as the _Generator_, is trained to produce synthetic information which the second model, the _Discriminator_, evaluates for authenticity. The two models are trained in competition (i.e., adversarial), with the generator working to produce data that the discriminator is unable to classify as synthetic.