## 4 Discussion  
Many algorithms require large amounts of data in order to efficiently extract information, a task which is particularly difficult when considering data derived from the fossil record. To confront this topic, this study presents a new integration of AIAs into archaeological and paleontological sciences. Here GANs have been shown to be a new and valuable tool for the modelling and augmentation of GM data. Moreover, these algorithms can additionally be employed on a number of different types of datasets and applications; whether this be the handling of paleounthropological, biological, taphonomic or lithic specimens via GM landmark data. To demonstrate this latter point, and applying typical geometric morphometric techniques for classification on the [PERSON] and Dryden [75] Hominoid skull dataset, the present study is able to increment balanced accuracy of traditional LDA up to ca. 5% (Figure 10a) with a significant increase in generalization (Figure 10b,c). For this demonstration LDA was trained using a traditional approach [11], as well as an augmented approach based on Machine Teaching [43]. It can be seen how applying Machine Teaching using 100 realistic synthetic points per sample for training, and the original data was used for testing, helps the generalization process (Figure 10b) while providing clearer boundaries for each of the sample domains (Figure 10c). Figure 10: Example of the Augmentation (_LSGAN_\(\times\)100) of O’Higgins and Dryden’s Hominoid dataset comparing gorilla (_Gorilla gorilla_), chimpanzee (_Pan troglodytes_) and orangutan (_Pongo pygmaeus_) skulls [75]. (**A**) Example of both the original and augmented datasets. Percentages next to each group represent the accuracy obtained by a more traditional Linear Discriminant Analysis algorithm. (**B**) Boxplots presenting the loss and confidence of predictions made using both the original and the augmented data set. To the right Receiver Operator Characteristic curves describe the overall performance of models on both datasets. (**C**) Decision boundaries drawn by LDA across PCI (\(x\)-axis) and PC2 (\(y\)-axis). The upper panel and lower panel of C represent the original and the augmented datasets respectively. It is important to point out, however, that this is not the solution to all sample-size related issues, and a number of components have to be discussed before more advanced applications can be carried out. Missing data and the availability of fossil finds are a major handicap in prehistoric research. This is increasingly relevant when considering fossils of older ages, such as individuals of the _Australopithecus, Paranthropus_ and early _Homo_ genera. In a number of cases, for example, the representation of _Australopithecine_ or _Homo erectus/ergaster_ specimens may not even surpass 10 individuals [76; 77; 78], while _Homo sapiens_ specimens are in abundance. In these cases, and in accordance with the data presented here, GANs would not be able to successfully augment the targeted minority distributions from scratch. Other options, however, could entail the use of algorithms for pre-processing, using variations of the Synthetic Minority Oversampling Technique (SMOTE & Borderline-SMOTE) [79; 80; 81], or the adapted version Adaptive Synthetic Sampling (ADASYN) [82]. Both SMOTE and ADASYN are useful, easy to implement algorithms that augment minority samples in imbalanced datasets. SMOTE generates synthetic data in the feature spaces that join data points (e.g., according to \(k\) nearest-neighbor theory), thus filling in regions of the target domain [79; 80; 81]. ADASYN takes this a step further by modelling on sample distributions based on data-density [82]. Both are valuable algorithms that have become popular in imbalanced learning tasks, generally improving predictive model generalization. Nevertheless, their application should be confronted conservatively. Preliminary experiments within this study found that resampling via bootstrapping prior to the training of GANs resulted in severe mode collapse.