{"id":3320,"date":"2025-10-06T16:10:26","date_gmt":"2025-10-06T14:10:26","guid":{"rendered":"https:\/\/tecnologia.euroinnova.com\/entrenamiento-de-modelos-de-ia-sin-etiquetado-de-datos-usando-estrategias-zero-shots\/"},"modified":"2025-10-07T14:50:43","modified_gmt":"2025-10-07T12:50:43","slug":"inteligencia-artificial-zero-shots","status":"publish","type":"post","link":"https:\/\/tecnologia.euroinnova.com\/fr\/inteligencia-artificial-zero-shots","title":{"rendered":"Formation de mod\u00e8les d'IA sans \u00e9tiquetage des donn\u00e9es \u00e0 l'aide de strat\u00e9gies Zero-shots"},"content":{"rendered":"
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El aprendizaje autom\u00e1tico<\/strong> ha evolucionado significativamente en los \u00faltimos a\u00f1os, permitiendo que los modelos de lenguaje (<\/strong><\/span>LLMs<\/strong><\/span>)<\/strong> y la visi\u00f3n artificial realicen tareas complejas con gran precisi\u00f3n. <\/span> <\/span><\/span><\/p>\n<\/div>\n

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Sin embargo, muchas t\u00e9cnicas de aprendizaje supervisado dependen de grandes conjuntos de datos etiquetados, lo que supone una limitaci\u00f3n en t\u00e9rminos de escalabilidad y aplicabilidad a nuevos dominios. <\/span> <\/span><\/span><\/p>\n<\/div>\n

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En este contexto, el aprendizaje Zero-<\/strong><\/span>Shot<\/strong><\/span> (ZSL)<\/strong> se trata de una estrategia que permite que los modelos realicen predicciones sobre clases o tareas para las que no han sido entrenados expl\u00edcitamente. <\/span> <\/span><\/span><\/p>\n<\/div>\n

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Sigue leyendo para descubrir c\u00f3mo se implementa este tipo de aprendizaje en el entrenamiento de modelos de inteligencia artificial.<\/span> <\/span><\/span><\/p>\n<\/div>\n

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Zero-<\/strong><\/span>Shot<\/strong><\/span> <\/strong><\/span>Learning<\/strong><\/span>: Aprendizaje sin necesidad de datos etiquetados<\/strong><\/span> <\/strong><\/span><\/span><\/p>\n<\/div>\n

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El aprendizaje Zero-<\/strong><\/span>Shot<\/strong><\/span> es una t\u00e9cnica o estrategia dentro de la inteligencia artificial que permite a un modelo realizar inferencias sobre datos que nunca ha visto durante el entrenamiento<\/strong>. <\/span> <\/span><\/span><\/p>\n<\/div>\n

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A diferencia del enfoque tradicional basado en grandes vol\u00famenes de datos de entrenamiento etiquetados, el ZSL permite que los modelos generalicen su conocimiento<\/strong> a nuevos conceptos sin requerir muestras previas espec\u00edficas.<\/span> <\/span><\/span><\/p>\n<\/div>\n

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Esto es posible gracias a representaciones sem\u00e1nticas avanzadas<\/strong>, que permiten a los modelos de lenguaje y visi\u00f3n conectar informaci\u00f3n nueva con conocimiento adquirido previamente. <\/span> <\/span><\/span><\/p>\n<\/div>\n

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Por ejemplo, si un modelo ha sido entrenado previamente en reconocer im\u00e1genes de perros y gatos, pero nunca ha visto una imagen de un lobo, el ZSL puede permitirle clasificar correctamente a este \u00faltimo bas\u00e1ndose en descripciones textuales o atributos compartidos con perros y gatos.<\/span> <\/span><\/span><\/p>\n<\/div>\n

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\u00bfC\u00f3mo aprenden los modelos de lenguaje (<\/strong><\/span>LLMs<\/strong><\/span>) a generalizar?<\/strong><\/span> <\/strong><\/span><\/span><\/p>\n<\/div>\n

El aprendizaje autom\u00e1tico ha evolucionado significativamente en los \u00faltimos a\u00f1os, permitiendo que los modelos de lenguaje (LLMs) y la visi\u00f3n […]<\/p>\n","protected":false},"author":2,"featured_media":993,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[1],"tags":[],"class_list":["post-3320","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-sin-categorizar"],"acf":[],"_links":{"self":[{"href":"https:\/\/tecnologia.euroinnova.com\/fr\/wp-json\/wp\/v2\/posts\/3320","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/tecnologia.euroinnova.com\/fr\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/tecnologia.euroinnova.com\/fr\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/tecnologia.euroinnova.com\/fr\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/tecnologia.euroinnova.com\/fr\/wp-json\/wp\/v2\/comments?post=3320"}],"version-history":[{"count":0,"href":"https:\/\/tecnologia.euroinnova.com\/fr\/wp-json\/wp\/v2\/posts\/3320\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/tecnologia.euroinnova.com\/fr\/wp-json\/wp\/v2\/media\/993"}],"wp:attachment":[{"href":"https:\/\/tecnologia.euroinnova.com\/fr\/wp-json\/wp\/v2\/media?parent=3320"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/tecnologia.euroinnova.com\/fr\/wp-json\/wp\/v2\/categories?post=3320"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/tecnologia.euroinnova.com\/fr\/wp-json\/wp\/v2\/tags?post=3320"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}