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CAT: Umbral adaptativo consciente de la clase para una generalización de dominio semisupervisada robusta

Sumaiya Zoha1, Jeong-Gun Lee2, Young-Woong Ko2

  • 1Department of Computer Science and Engineering, Ahsanullah University of Science and Technology, Dhaka, Bangladesh.

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|September 4, 2025
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio presenta el CAT, un nuevo método de generalización de dominio semisupervisado que utiliza el umbral adaptativo y el refinamiento de pseudoetiquetas. Logra un fuerte rendimiento de generalización con datos etiquetados limitados, superando los desafíos de los cambios de dominio.

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Área de la Ciencia:

  • Visión por computadora
  • Aprendizaje automático
  • Inteligencia artificial

Sus antecedentes:

  • La Generalización de Dominio (DG) tiene como objetivo transferir conocimiento a través de dominios, pero requiere datos etiquetados extensos.
  • Los datos etiquetados de alta calidad son costosos y requieren mucha mano de obra, lo que limita las aplicaciones prácticas de la DG.
  • La Generalización de Dominio Semi-supervisada (SSDG) ofrece una alternativa eficiente para las etiquetas.

Objetivo del estudio:

  • Investigar un problema práctico de SSDG bajo un paradigma de etiqueta eficiente.
  • Proponer un nuevo método, CAT, para un rendimiento de generalización competitivo con datos etiquetados limitados.
  • Abordar las limitaciones de los métodos anteriores, incluidos los umbrales fijos y las pseudoetiquetas ruidosas.

Principales métodos:

  • Aproveche el aprendizaje semisupervisado con datos etiquetados limitados.
  • Emplear umbrales adaptativos para la generación de pseudoetiquetas de alta calidad con diversidad de clases.
  • Utilice técnicas de refinamiento de etiquetas ruidosas para mejorar la fiabilidad de las pseudoetiquetas.

Principales resultados:

  • CAT logra un rendimiento de generalización competitivo bajo cambios de dominio.
  • Se ha demostrado un rendimiento superior en los conjuntos de datos de referencia: PACS (+3,45%), OfficeHome (+9,47%), y miniDomainNet (+10,90%).
  • Destaca la eficacia en el logro de una generalización sólida a pesar de los cambios de dominio.

Conclusiones:

  • CAT proporciona una solución sencilla pero muy eficaz para las tareas de SSDG.
  • El método supera con éxito la dependencia de umbrales fijos y la sensibilidad a las pseudoetiquetas ruidosas.
  • Lograr una generalización sólida en entornos de etiquetado eficiente, mejorando la aplicabilidad práctica de la DG.