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Datos dispersos, resultados ricos: aprendizaje semi-supervisado de pocos disparos a través de la traducción de
Guido Manni1, Clemente Lauretti2, Loredana Zollo2
1Unit of Artificial Intelligence and Computer Systems, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy; Unit of Advanced Robotics and Human-Centered Technologies, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy.
Resumen
Este estudio presenta un novedoso marco de aprendizaje semi-supervisado basado en GAN para imágenes médicas, que mejora significativamente la clasificación con datos etiquetados mínimos. El enfoque se destaca en escenarios con pocos datos, ofreciendo una solución práctica para anotaciones costosas.
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