# Use of Generative Adversarial Networks (GAN) for
###### Abstract  
Deep learning models are based on a combination of neural network architectures, optimization parameters and activation functions. All of them provide exponential combinations whose computational fitness is difficult to pinpoint. The intricate resemblance of the microscopic features that are found in bone surface modifications make their differentiation challenging, and determining a baseline combination of optimizers and activation functions for modeling seems necessary for computational economy. Here, we experiment with combinations of the most resolutive activation functions (relu, swish, and mish) and the most efficient optimizers (stochastic gradient descent (SGD) and Adam) for bone surface modification analysis. We show that despite a wide variability of outcomes, a baseline of relu-SGD is advised for raw bone surface modification data. For imbalanced samples, augmented datasets generated through generative adversarial networks are implemented, resulting in balanced accuracy and an inherent bias regarding mark replication. In summary, although baseline procedures are advised, these do not prevent to overcome [PERSON]'s \"no free lunch\" theorem and extend it beyond model architectures.  
generative adversarial networks; optimizer; activation function; neural networks; computer vision; tablonomy +
Footnote †: journal:  
ensemble learning procedures, the selection of activation functions and the choice of optimizers, among others. There currently is no protocol for which of these combinations is most adequate in the use of BSM images for tqnohonomic analyses. Most of the models mentioned above are based on imbalanced samples and their balanced accuracy is variable. In the present work, our objectives are two-fold: we intend to provide a baseline protocol based on combinations of different architectures, activation functions and optimizers, and we will show the convenience of including, in these protocols, data augmentation procedures for coping with unbalanced samples.  
Image augmentation has frequently been done using morphing of the currently existing datasets by modifying spatial properties, such as rotation, zoom, flipping, cropping, translation, kernel filters, noise introduction, and others [7; 8; 9; 10]. DCNNs need large datasets for training in order to efficiently learn, and small samples hinder the process by either biasing it or by overfitting the training and underperforming on the testing. Data augmentation has been essential to avoid these issues. One major improvement over traditional image augmentation techniques has been the development of generative adversarial networks (GAN) [11; 12]. GANs are capable of, not just modifying existing images, but creating new images that could diversify within sample variability. This has boosted the efficiency of medical imaging, where samples for specific pathologies are always limited [13; 14; 15; 16]. Here, we will use GAN data augmentation with the goal of improving existing models and making accuracy more balanced. However, we will present some problems associated with GAN methods that require some caution in how these methods are implemented in BSM studies.