## 4 Discussion
This can be theoretically explained by the manner in which bootstrapping is over-inflating the domain and highlighting very specific regions which the model then learns from. This results in overfitting as the model is repeatedly learning to map out the same value multiple times, boosting the probability of mode collapse through an enhanced lack of variation in the original trainset. Considering how SMOTE and ADASYN produce more \"meaningful\" data [44; 79], these algorithms are more likely to aid the training process rather than produce the adverse effect. Nevertheless, overuse of SMOTE/ADASYN is likely to have a similar effect to bootstrapping, where linear regions of feature space between data points are enhanced while other regions are left empty. Through this, the current study proposes that a conservative use of SMOTE or ADASYN variants prior to the training of GANs may be able to boost performance on overly-scarce datasets (e.g., the cases of [76; 77; 78]). This practice would be able to augment minority samples to a suitable threshold (\(n=30\)), preparing the dataset for more complex generative modelling and enabling an improved generalization of any predictive models used in analyses that follow. From a similar perspective, the use of Bayesian Inference Algorithms such as Markov Chain Monte Carlo (MCMC) and Metropolis-Hastings algorithms have also been known to effectively model from multiple types of probability distributions [83; 84; 85]. In some cases, it may be possible to use these approaches to sample from the probability distribution at hand, and produce simulated information from the target distribution which would essentially be more realistic than simple bootstrap approaches. Further research into how these approaches may be applied could provide a powerful insight into GAN alternatives for different types of numerical data in GM. In the general context of computational modelling, common criticism of neural network applications in archaeology and paleoanthropology argue that GM datasets are generally insufficient for the training of AIAs. This is based on the fact that most DL algorithms require much more data to avoid overfitting. From this point of view, why would training a GAN on such little data be any different? The present study proves that this is not an issue, considering how, with only 30 individuals, GANs are still able to produce highly realistic synthetic data (_LSGAN_ rTOST \(p=3.8\times 10^{-22}\)). In common DL literature, state-of-the-art models are reported to obtain \(\approx\)80% accuracy when trained on thousands to millions of specimens [25]. It is important to consider, however, that in the majority of these cases AIAs are trained on _images_ (i.e., computer vision applications). To provide an example, [PERSON] et al. [54] present a GAN capable of producing hyper-realistic fake images of people's faces, building from a subset of the CelebA-HQ dataset using \(\approx\)30,000 images. Two main components must be considered in order to understand why such a large dataset is required for their model;  
* [PERSON] et al. [54] present a GAN capable of producing high resolution 1024x1024 pixel RGB images. In computer vision applications, each image is conceptualized as a multidimensional numeric matrix (i.e., a tensor). Each of the numbers within the tensor can essentially be considered a variable, resulting in a dataset of approx. three million variables per individual photo. * In order to efficiently map out these three million numeric values, the featured GAN uses progressively growing convolutional layers (n\({}^{\circ}\)layers \(\approx\) 60) with 23.1 million adjustable parameters. * The present study uses feature spaces that have already undergone dimensionality reduction derived from GM landmark data. In the case of the largest dataset, this results in a target vector of 60 variables that need to be generated. The present study additionally only uses fully connected layers with no convolutional filters, resulting in a model of \(<\)11,000 adjustable parameters.