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Unsupervised Clustering of Roman Potsherds via Variational Autoencoders
In this paper we propose an artificial intelligence imaging solution to support archaeologists in the classification task of Roman commonware potsherds.
Unsupervised clustering of Roman pottery profiles from their SSAE representation
The partiality and the handcrafted variance of the shape fragments within this new database make their unsupervised clustering a very challenging problem: profile similarities are thus explored via the hierarchical clustering of non-linear features learned in the latent representation space of a stacked sparse autoencoder (SSAE) network, unveiling new profile matches.
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