Modelos Generativos en el Aprendizaje Automático y su  aplicación a la generación de Imágenes Digitales

Oscar Contreras Carrasco

doi:10.52428/20758944.v17i51.110

Autores/as

Oscar Contreras Carrasco Universidad Privada del Valle

DOI:

https://doi.org/10.52428/20758944.v17i51.110

Palabras clave:

Redes neuronales, Deep Learning, Modelos generativos, Redes convolucionales, GAN, VAE

Resumen

En el ámbito del Aprendizaje Automático se identifican dos tipos de algoritmos desde el punto de vista de la naturaleza de las salidas que estos proporcionan. Los modelos discriminativos asocian un dato proporcionado a una respuesta, mientras que los modelos generativos generan nuevos datos en base a una distribución probabilística de respuestas o variables latentes. Por otra parte, en años recientes, se han dado importantes avances en el Deep Learning, que es el estudio de las redes neuronales profundas. En este contexto, las redes convolucionales han ganado considerable terreno en diversas tareas relacionadas al análisis de imágenes. Entre las varias aplicaciones de las redes convolucionales se mencionan: clasificación de imágenes, detección de objetos, segmentación de instancias, reconocimiento facial, entre otros varios. Sin embargo, en el ámbito del Deep Learning, no solamente se han tenido importantes avances con relación a los puntos aquí mencionados, sino también en cuanto a la habilidad de los modelos en generar nuevas imágenes. De ese modo, hoy en día se tiene a disposición una variedad de modelos generativos para diversos propósitos, con aplicaciones tales como la creación de imágenes faciales de personas que no existen en la realidad. Es así que el propósito del presente artículo es realizar un análisis de diversos modelos generativos para imágenes digitales, así como de las bases teóricas que sustentan a los modelos generativos en el Deep Learning. El enfoque de análisis se centra en dos modelos esenciales: Las redes adversarias generativas (GAN) y los autoencoders variacionales (VAE).

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Citas

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