Actualizado:
Learn how k-means, k-medoids and aggloKNN convert relationships between tabular samples into grayscale synthetic images, including distance metrics, reference ordering and RBF smoothing.
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Learn how the new TINTOlib Clusters method converts distances, membership probabilities, density estimates and latent factors into synthetic images for vision-based deep learning.
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A practical and methodological comparison of TINTO, REFINED and IGTD for transforming tabular data into synthetic images and applying vision-based deep learning models.
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How TINTOlib transforms tabular data into synthetic images, which spatial encoding methods should be preferred, and how hybrid neural networks and explainable AI complete the modelling pipeline.
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Why deep learning still struggles with tabular data, and why synthetic image representations provide a promising bridge between structured data and computer vision architectures.
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An introduction to TINTOlib: why tabular data requires spatial encoding, how to generate synthetic images avoiding data leakage, and a complete end-to-end CNN pipeline in PyTorch.
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Technical introduction to TINTOlib, a Python framework for transforming tabular data into synthetic images and applying CNN-based deep learning architectures.