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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Cognitive Learning01:21

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Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
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Human development is typically examined across three main domains: physical, cognitive, and socio-emotional. These domains represent the significant areas of change and continuity throughout the lifespan, from infancy to late adulthood.
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Marco de aprendizaje dual de dominio-atributo con adaptadores asíncronos para adaptación continua en tiempo de prueba

Yuntong Tian, Kang Li, Tianyang He

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
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    Resumen
    Este resumen es generado por máquina.

    Este estudio presenta un novedoso marco de aprendizaje dual de dominio-atributo para la adaptación continua de dominio en tiempo de prueba (CTTA). Mitiga eficazmente el olvido catastrófico y la acumulación de errores en entornos dinámicos empleando actualizaciones asíncronas independientes para el conocimiento específico del dominio y el invariante del dominio.

    Palabras clave:
    adaptación continua en tiempo de pruebaaprendizaje dual de dominio-atributoadaptadores asíncronosolvido catastróficoacumulación de erroresentornos dinámicosinteligencia artificialaprendizaje automáticovisión por computadora

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    Área de la Ciencia:

    • Inteligencia Artificial; Aprendizaje Automático; Visión por Computadora

    Sus antecedentes:

    • La adaptación continua de dominio en tiempo de prueba (CTTA) aborda la implementación de modelos en entornos dinámicos y no estacionarios adaptando modelos preentrenados a dominios de destino no etiquetados en evolución.
    • Los métodos CTTA actuales a menudo sincronizan el aprendizaje de información específica del dominio (DS) e invariante del dominio (DI), lo que lleva a la acumulación de errores y al olvido catastrófico debido a las discrepancias entre dominios.

    Objetivo del estudio:

    • Proponer un novedoso marco de aprendizaje dual de dominio-atributo inspirado en el Modelo de Memoria de Atributos (AMM) del cerebro.
    • Permitir que los modelos aprendan nuevo conocimiento sin olvidar información previa durante la adaptación continua.

    Principales métodos:

    • Descomposición de CTTA en un sistema de aprendizaje basado en eventos (ELS) para el estilo DS y un sistema de aprendizaje basado en conocimiento (KLS) para la estructura DI.
    • ELS crea activamente adaptadores para nuevos dominios basándose en cambios de distribución, mientras que KLS utiliza un adaptador compartido para el conocimiento general.
    • Implementación de un solucionador de conflictos de gradientes para gestionar conflictos entre el conocimiento DI pasado y actual para una adaptación sin interferencias.

    Principales resultados:

    • El marco propuesto demostró un rendimiento superior en tareas de CTTA de segmentación y clasificación.
    • Superó a los enfoques de vanguardia en cuatro conjuntos de datos de referencia.
    • Abordó con éxito los problemas de acumulación de errores y olvido catastrófico inherentes a los métodos CTTA anteriores.

    Conclusiones:

    • El marco de aprendizaje dual de dominio-atributo ofrece una solución eficaz para CTTA al separar y gestionar el conocimiento DS y DI de forma asíncrona.
    • El mecanismo de actualización asíncrona independiente, inspirado en la neurociencia, mejora la adaptabilidad del modelo y la retención del conocimiento en dominios en evolución.
    • Este enfoque representa un avance significativo en el manejo de entornos no estacionarios para modelos de aprendizaje automático.