I remember when we studied ceramics in university. The process of ceramic drawing, its subsequent digitization, and finally using ceramic guides to identify the typology. By the time I graduated, there were already statistical works where clusters were used to unravel the derivations of ceramic designs as well as the influences of different cultures. Works like those by Contreras Cortés (1984) brought us closer to this collaboration between ceramic study and statistical techniques. Forty years have now passed since then, and the inevitable question is, how much have we advanced in methods for classifying ceramics since the 1980s? In this work, we will continue with the line of previous articles where we highlighted works that reflect how artificial intelligence is helping digital humanities to answer old questions and generate new ones. Therefore, if in the first article we delved into introductory concepts to differentiate between artificial intelligence (AI), Machine Learning (ML) and deep learning (DL) (Luengo, 2023) and subsequently addressed the application of algorithms such as the Ithaca algorithm on epigraphic material (Luengo, 2024), in this occasion we will focus on Iberian ceramics with algorithms called IberianGAn and IberianVoxel.
Let's start with the first one, what exactly is IberianGAn and where does its name come from?
In short, IberianGAN is a deep learning model that uses Generative Adversarial Networks (GAN) to reconstruct profiles of Iberian ceramics from fragments. And obviously, there's the name: "Iberian" because it specializes in Iberian ceramics and "GAN" because it uses Generative Adversarial Networks. Let's go a step further, what do we understand by Iberian ceramics and what by a GAN?
Iberian pottery refers to the pottery created by the Iberian culture, a group of peoples who mainly inhabited the south and east of the peninsula, including the region of the upper Guadalquivir valley (Navarro et al., 2022, p. 3).
On the other hand, Generative Adversarial Networks (GANs) are a type of deep learning model that can generate new data similar to the data it was trained on. Its functioning can be understood using the analogy of forger and policeman. In this analogy, the forger (encoder) seeks to create copies of objects, such as Iberian pottery profiles in this case, so convincing they could pass as authentic (scheme shown in figure 1). Meanwhile, the policeman (decoder) is responsible for analyzing the objects and determining if they are real or fakes (Gunasekaran, 2023).
But this leads to the next question, why would we want to use a generative model capable of creating or rather inventing ceramic pieces that appear faithful to existing ones? How does it help in identifying Iberian pottery found at archaeological sites?
The main reason lies in the possibility of virtually reconstructing fragmented pieces and extracting valuable information from the original piece. Thus, GANs contribute in different ways:
On one hand, completing missing information: at archaeological sites, most ceramics are found in fragments, which makes their analysis difficult. GANs like IberianGAN (Navarro et al., 2022) and IberianVoxel (Navarro et al., 2023), trained on a large database of complete profiles and fragments of Iberian pottery, can generate the missing parts of a profile based on the available fragment information. This allows archaeologists to visualize the whole piece even if it hasn't been found intact. This virtual reconstruction aids in morphological analysis, which is crucial. The shape of a ceramic piece offers information about its function, the era it belongs to, and even its region of origin. Virtual reconstruction of the piece allows analyzing its morphological characteristics and comparing them with other ceramics, facilitating identification. For example, IberianGAN has been evaluated through morphometric analyses, confirming its ability to generate fragments with realistic shapes (Navarro et al., 2022, p. 4). With all this, we help define the Iberian style better. Generative models trained on examples of Iberian pottery learn the characteristics that define this particular style. By generating new pieces, these features are maintained, allowing archaeologists to determine if a fragment belongs to this style. Finally, this model would allow for the creation of a broader virtual database. GANs can generate thousands of virtual ceramic profiles, creating a much larger database than real profile data. This is probably one of the most important factors in our view, as we also commented on parallel reconstructions (Luengo, 2024). As a result, this virtual database can be used to search for matching fragments, analyze form and decoration variability, and identify patterns that might go unnoticed with limited datasets.
But we cannot simply accept that the reconstructions made by the model are valid or that its deductions are feasible; we need to verify, quantify, and ultimately confirm (with greater or lesser precision) the confidence in the generated data and conclusions. So how can we evaluate the GAN-generated reconstructions to indicate their quality? For this, researchers have used different metrics including the Fréchet inception distance (FID), the root mean squared error (RMSE) in semi-reference point analysis, qualitative evaluation by expert archaeologists, among others (Navarro et al., 2022, p. 4). These metrics help determine the similarity between generated and real ceramics both in terms of general form and specific Iberian style, helping to narrow down the degree of certainty in the conclusions obtained.
To all this, it is important to highlight that only 752 images were used to train IberianGAN. And if we emphasize this, it's because with such a limited number of ceramic profiles, it is possible to approach training a model like IberianGAN with these results, it is evident that the discoveries machine learning can bring to archaeology are immense if we could increase the databases. It is also worth noting that the team leading these studies has shared the dataset on which the model relies, as well as the code in Github, and the necessary models not only to validate it but also to contribute within the world of Open source to advance and improve research.
But if we talk about IberianGAN, we cannot avoid talking about IberianVoxel. While IberianGAN uses 2D images at all times, IberianVoxel dares with the three-dimensional world. Thus, while the first aims to recreate two-dimensional profiles, the second attempts to recreate the entire three-dimensional volume. Using the same model design? Mostly yes. Although both models use an encoder-decoder architecture, their specific implementation differs. IberianGAN uses a GAN with an encoder that processes an input image (the fragment) and a decoder that generates the missing part. IberianVoxel, on the other hand, uses a 3D AE-GAN, where the encoder processes a 32x32x32 voxel matrix and the decoder generates a similar voxel matrix representing the complete ceramic. Regarding the data used for training, there are some similarities. In this case, IberianVoxel used 1001 voxelized samples (Navarro et al., 2023, p. 5834). The metrics for evaluating the model were mainly the Sørensen-Dice index and the Mean Squared Error (MSE), among others.
Everything discussed so far is focused on Iberian pottery. The question arises as to whether there are works beyond this ceramic type. The answer is a resounding yes. There are numerous studies, and more every day, that build upon these starting points to apply the benefits of machine learning to different sets of ceramics. We have examples with Chinese Miaozigou culture pottery (Pang et al., 2024) or Roman Lusitanian amphorae (Santos et al., 2024), among many other examples.
The main conclusion of this text is that artificial intelligence, and in particular Generative Adversarial Networks (GANs), are having a significant impact on the field of archaeology, especially in reconstructing fragmented artifacts such as Iberian pottery. Models like IberianGAN and IberianVoxel demonstrate not only the possibility of recreating two-dimensional ceramic profiles but also reconstructing three-dimensional objects from fragments. It seems that literature will soon be flooded with many works where these techniques are applied to various sets of ceramics. Hopefully, little by little, those collections will be shared, and we can have a broad and accessible digital ceramic corpus that facilitates research and allows for more ambitious analyses.
Bibliography
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