New Cloth Unto an Old Garment: SOM for Regeneration Learning

A recent paradigm called Regeneration Learning addresses generative problems where the target data (e.g., images) is more complex than the available input source. While current cross-modal representation and regeneration learning rely on supervised deep learning models, this paper aims to revisit the adequacy of unsupervised models in this field. In this regard, we propose a new unsupervised approach that utilizes the SOM as a heteroassociative memory model to learn cross-modal representations in a topologically coherent map. This approach enables bidirectional predictive/regenerative mapping between domains. We evaluate the potential of this method for an unsolved (so far!) practical problem in petroleum geoscience.