
Part 4 — RGB Fusion and Structural Stability in TINTOlib: mixMethod and SSIM-Based Automatic Selection
Final tutorial in the TINTOlib Clusters series, covering RGB multi-view fusion with mixMethod, channel interpretation and autom...
Read article →Technical tutorials, research notes and reproducible examples on TINTOlib, tabular-to-image transformation, synthetic image generation from tabular data, hybrid neural networks, explainable AI and applied deep learning.
By Manuel Castillo-Cara, PhD — Researcher and Professor at UNED, developer of TINTO and TINTOlib.

Final tutorial in the TINTOlib Clusters series, covering RGB multi-view fusion with mixMethod, channel interpretation and autom...
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Technical tutorial on the probabilistic, density-based and latent representations available in TINTOlib's Clusters class: Gauss...
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Technical comparison of the distance-based representations available in TINTOlib's Clusters class: k-means centroid distances, ...
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Technical introduction to the new Clusters method in TINTOlib, which transforms tabular data into grayscale or RGB synthetic im...
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Comparative analysis of TINTO, REFINED and IGTD, three representative tabular data into synthetic image transformation methods ...
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Part II of a theoretical and technical series on synthetic images for tabular data, focusing on TINTOlib, preferred spatial enc...
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Part I of a theoretical and technical series on why deep learning still struggles with tabular data, and why synthetic image re...
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An introduction to TINTOlib: why tabular data requires spatial encoding, how to generate synthetic images avoiding data leakage...
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Technical introduction to TINTOlib, a Python framework for transforming tabular data into synthetic images and applying CNN-bas...
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