{"id":11850,"date":"2024-04-30T12:48:52","date_gmt":"2024-04-30T12:48:52","guid":{"rendered":"https:\/\/dailyai.com\/?p=11850"},"modified":"2024-04-30T15:58:32","modified_gmt":"2024-04-30T15:58:32","slug":"googles-gecko-benchmark-identifies-best-ai-image-generator","status":"publish","type":"post","link":"https:\/\/dailyai.com\/es\/2024\/04\/googles-gecko-benchmark-identifies-best-ai-image-generator\/","title":{"rendered":"La prueba Gecko de Google identifica el mejor generador de im\u00e1genes por IA"},"content":{"rendered":"

Google DeepMind<\/span><\/span> ha presentado Gecko, una nueva referencia para evaluar de forma exhaustiva los modelos de conversi\u00f3n de texto en imagen (T2I).<\/strong><\/p>\n

En los \u00faltimos dos a\u00f1os, hemos visto generadores de im\u00e1genes de IA como DALL-E<\/span><\/span><\/a> y A mitad de camino<\/a> mejoran progresivamente con cada versi\u00f3n.<\/p>\n

Sin embargo, decidir cu\u00e1l de los modelos subyacentes que utilizan estas plataformas es el mejor ha sido en gran medida subjetivo y dif\u00edcil de evaluar comparativamente.<\/p>\n

Afirmar a grandes rasgos que un modelo es \"mejor\" que otro no es tan sencillo. Cada modelo destaca en distintos aspectos de la generaci\u00f3n de im\u00e1genes. Uno puede ser bueno en la representaci\u00f3n de texto, mientras que otro puede ser mejor en la interacci\u00f3n con objetos.<\/p>\n

Un reto clave al que se enfrentan los modelos T2I es seguir cada detalle de la indicaci\u00f3n y que \u00e9stos se reflejen con precisi\u00f3n en la imagen generada.<\/p>\n

Con Gecko, el DeepMind<\/span><\/span> investigadores han creado un referencia<\/a> que eval\u00faa las capacidades de los modelos T2I de forma similar a como lo hacen los humanos.<\/p>\n

Competencias<\/h2>\n

En primer lugar, los investigadores definieron un amplio conjunto de habilidades relevantes para la generaci\u00f3n de T2I. Entre ellas se incluyen la comprensi\u00f3n espacial, el reconocimiento de acciones, la representaci\u00f3n de textos y otras. A continuaci\u00f3n, las dividieron en subhabilidades m\u00e1s espec\u00edficas.<\/p>\n

Por ejemplo, en representaci\u00f3n de texto, las subhabilidades podr\u00edan incluir la representaci\u00f3n de diferentes fuentes, colores o tama\u00f1os de texto.<\/p>\n

A continuaci\u00f3n, se utiliz\u00f3 un LLM para generar indicaciones que pusieran a prueba la capacidad del modelo T2I en una habilidad o subhabilidad espec\u00edfica.<\/p>\n

Esto permite a los creadores de un modelo T2I determinar no s\u00f3lo qu\u00e9 destrezas suponen un reto, sino a qu\u00e9 nivel de complejidad una destreza se convierte en un reto para su modelo.<\/p>\n

\"\"
El marco de referencia Gecko utiliza un conjunto de datos de habilidades y subhabilidades (a), una puntuaci\u00f3n Likert humana de la precisi\u00f3n de las im\u00e1genes (b), consultas generadas por LLM para el an\u00e1lisis VQA, y da como resultado m\u00e9tricas exhaustivas que se correlacionan con las evaluaciones humanas. Fuente: arXiv<\/figcaption><\/figure>\n

Humano vs Autoevaluaci\u00f3n<\/h2>\n

Gecko tambi\u00e9n mide la precisi\u00f3n con la que un modelo T2I sigue todos los detalles de una pregunta. Una vez m\u00e1s, se utiliz\u00f3 un LLM para aislar los detalles clave de cada pregunta y generar un conjunto de preguntas relacionadas con esos detalles.<\/p>\n

Estas preguntas pueden ser tanto sencillas y directas sobre elementos visibles en la imagen (por ejemplo, \"\u00bfHay un gato en la imagen?\") como m\u00e1s complejas, que ponen a prueba la comprensi\u00f3n de la escena o las relaciones entre objetos (por ejemplo, \"\u00bfEst\u00e1 el gato sentado encima del libro?\").<\/p>\n

A continuaci\u00f3n, un modelo de respuesta a preguntas visuales (VQA) analiza la imagen generada y responde a las preguntas para comprobar la precisi\u00f3n con la que el modelo T2I alinea su imagen de salida con una pregunta de entrada.<\/p>\n

Los investigadores recopilaron m\u00e1s de 100.000 anotaciones humanas en las que los participantes puntuaban una imagen generada en funci\u00f3n de lo alineada que estuviera con criterios espec\u00edficos.<\/p>\n

Se ped\u00eda a los humanos que tuvieran en cuenta un aspecto concreto de la pregunta y puntuaran la imagen en una escala de 1 a 5 en funci\u00f3n de su adecuaci\u00f3n a la pregunta.<\/p>\n

Utilizando las evaluaciones anotadas por humanos como patr\u00f3n oro, los investigadores pudieron confirmar que su m\u00e9trica de autoevaluaci\u00f3n \"se correlaciona mejor con las valoraciones humanas que las m\u00e9tricas existentes para nuestro nuevo conjunto de datos\".<\/p>\n

El resultado es un sistema de evaluaci\u00f3n comparativa capaz de poner cifras a los factores espec\u00edficos que hacen que una imagen generada sea buena o no.<\/p>\n

Esencialmente, Gecko punt\u00faa la imagen de salida de un modo que se ajusta a c\u00f3mo decidimos intuitivamente si estamos satisfechos o no con la imagen generada.<\/p>\n

\u00bfCu\u00e1l es el mejor modelo de conversi\u00f3n de texto en imagen?<\/p>\n

En su papel<\/a>los investigadores concluyeron que el modelo Muse de Google supera a Stable Diffusion 1.5 y SDXL en la prueba Gecko. Puede que sean parciales, pero los n\u00fameros no mienten.<\/p>","protected":false},"excerpt":{"rendered":"

DeepMind de Google ha lanzado Gecko, un nuevo punto de referencia para evaluar de forma exhaustiva los modelos de conversi\u00f3n de texto en imagen (T2I). En los \u00faltimos dos a\u00f1os, hemos visto c\u00f3mo generadores de im\u00e1genes de IA como DALL-E y Midjourney mejoraban progresivamente con cada versi\u00f3n lanzada. Sin embargo, decidir cu\u00e1l de los modelos subyacentes que utilizan estas plataformas es el mejor ha sido en gran medida subjetivo y dif\u00edcil de comparar. Afirmar a grandes rasgos que un modelo es \"mejor\" que otro no es tan sencillo. Cada modelo destaca en distintos aspectos de la generaci\u00f3n de im\u00e1genes. Uno puede ser bueno en la representaci\u00f3n de texto, mientras que otro puede ser mejor en la interacci\u00f3n con objetos. Un reto clave de los modelos T2I<\/p>","protected":false},"author":6,"featured_media":11853,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[147,107,102,165],"class_list":["post-11850","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-deepmind","tag-generative-ai","tag-google","tag-image-generation"],"yoast_head":"\nGoogle\u2019s Gecko benchmark identifies best AI image generator | DailyAI<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dailyai.com\/es\/2024\/04\/googles-gecko-benchmark-identifies-best-ai-image-generator\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Google\u2019s Gecko benchmark identifies best AI image generator | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Google\u2019s DeepMind released Gecko, a new benchmark for comprehensively evaluating AI text-to-image (T2I) models. 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