Exploración de bases de datos para la formación de modelos de aprendizaje automático en la industria de la moda
DOI:
https://doi.org/10.29147/datjournal.v9i2.877Palabras clave:
Machine Learning in Fashion, Artificial intelligence, Technological innovation, Data base, Supervised learningResumen
El creciente interés en aplicar el aprendizaje automático (ML) a la moda destaca la importancia de utilizar datos etiquetados para desarrollar modelos, facilitando la replicación de la investigación y automatizando el análisis de nuevos datos, como las imágenes de desfiles de moda disponibles en línea. A pesar de esta necesidad, pocos estudios, especialmente en Brasil, exploran metodológicamente la intersección entre moda y AM. Esta investigación tiene como objetivo proporcionar una descripción general de las bases de datos en línea para entrenar modelos de ML. Una revisión sistemática identificó 26 artículos que utilizan estas bases de datos, como Fashion-MNIST y DeepFashion2. El análisis de contenido reveló que estas bases de datos, incluidas Polyvore y Fashion Image Dataset, tienen diversas aplicaciones, destacando el potencial transformador de la fabricación aditiva en la moda y fomentando innovaciones en diseño, producción y marketing en la industria de la moda.
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