# Use of Generative Adversarial Networks (GAN) for
## 4 Discussion  
The augmented dataset contained 1657 images, in which the three classes appeared balanced: 606 tooth marks, 488 cut marks and 563 trampling marks. The original imbalanced model, based on a smaller raw data sample, classified correctly 92% of the testing set [1]; however, its F1-score (0.73) was substantially lower. Split by class, cut marks were the best classified (F1 = 0.97), followed by tooth marks (F1 = 0.80), but trampling marks were frequently misclassified (F1 = 0.42), most of the time as tooth marks. This creates uncertainty as to the power of the classifications because of the imbalanced dataset. The original number of cut marks in the sample was eight times bigger than the trampling dataset. Likewise, it was almost five times bigger than the tooth mark subsample. Data augmentation using the generation of new images is essential to balance each class dataset. This usually results in higher accuracy in classification and more reliability in classification probabilities. In the present work, the GAN-augmented sample and models have yielded a  
Figure 6: Bayesian network selection by the hill-climbing algorithm on the GAN-augmented image data bank. Model (**A**), voted by the “loglik” score function; model (**B**), voted by the “BIC” function; model (**C**), voted by the “bdla” function; model (**D**), voted by the “bde” function.  
slightly lower global accuracy than when using just the raw data, but the balanced accuracy was systematically higher. The swish-Adam combination in the ResNet50 model yielded the most accurate (91.29%) architecture and the F1.score yielded the highest balanced accuracy (87%) (Table 3); this latter was significantly higher than when using the raw data. One biasing feature that we observed in our augmented sample is that the generator created preferentially trampling images from the original sample which showed the least resemblance to either cut marks or tooth marks. This created an artificial sample where these originally minority marks became predominant in the augmented sample; hence the higher balanced accuracy.  
Previously, the VGG16 model was used to preliminarily classify some controversial bone surface modifications from the archaeological record [1]. These marked bone specimens are of extraordinary importance because they could potentially attest some of the \"first\" traces of human butchery in the locations where they were found. These involved specimens from Bluefish Caves (18,000 B.P., Yukon, Canada), purportedly belonging to the earliest presence of humans in the American continent [30], Anjohibe (1400-2000 B.C.) [31], Itampolo (1100-1800 B.P.) [32] and Christmas River (>10,000 B.P.) [33] (Madagascar)--interpreted as some of the earliest evidence of human presence on the island-, Dikika (3.2 Ma, Ethiopia)--potentially the first evidence of tone tool use- and Barranco Leon and Fuentenueva 3 (1.4 Ma, Orce, Spain)--presented as the oldest cut marks in Europe [34]. In order to assess architecture-model variability, here we used the most successful model (ResNet50) to tentatively classify some of these marks, bearing in mind that since these BSM were taken from published photographs not following the protocol applied to the experimental BSM, the conclusions are of limited value. Instead of using the swish-SGD combination, which yielded the highest accuracy (98.44%), we used the relu-SGD combination because its balanced accuracy was higher (80%) and then, less prone to misclassify classes. We only used a few of the archaeological marks that were interpreted by human experts as cut marks and that with the VGG16 model had previously been classified as non-anthropogenic [1]. The resulting classification did not vary from that obtained previously with the VGG16 model, using higher resolution images (Table 4). However, the probabilities became smaller (and so did the variance) because we were dealing with lower information images that also imparted some deformation over the original photographs.