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Prior work on texture analysis of historic, photographic papers has focused primarily on measures of texture similarity. However, automated grouping or clustering of photographic paper textures in a way that is meaningful to art conservators remains an open problem. In this work a deep learning approach to automated classification is presented, for clusters derived from a human sorting experiment...
In this paper we introduce the Tensor Deep Stacking Network (T-DSN) Toolkit, an implementation of the T-DSN deep learning architecture. The toolkit consists of a Python library and a set of accompanying helper scripts that allow you to train and evaluate T-DSN models. The toolkit is designed to be portable, modular, efficient and parallelized. Our goal for the toolkit is to promote research on this...
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