{"id":2680,"date":"2023-07-18T20:48:46","date_gmt":"2023-07-18T20:48:46","guid":{"rendered":"https:\/\/dailyai.com\/?p=2680"},"modified":"2023-07-18T21:23:28","modified_gmt":"2023-07-18T21:23:28","slug":"ai-image-generators-quality-suffers-when-trained-on-ai-generated-data","status":"publish","type":"post","link":"https:\/\/dailyai.com\/es\/2023\/07\/ai-image-generators-quality-suffers-when-trained-on-ai-generated-data\/","title":{"rendered":"La calidad de los generadores de im\u00e1genes de IA se resiente cuando se entrenan con datos generados por la IA"},"content":{"rendered":"

Un nuevo estudio demuestra que entrenar a los generadores de im\u00e1genes de IA con im\u00e1genes generadas por la IA acaba provocando una reducci\u00f3n significativa de la calidad del resultado.\u00a0<\/b><\/p>\n

Baraniuk y su equipo demostraron c\u00f3mo este problem\u00e1tico bucle de entrenamiento de IA afecta a las IA generativas, incluidos StyleGAN y los modelos de difusi\u00f3n. Estos son algunos de los modelos que se utilizan para los generadores de im\u00e1genes de IA como Stable Diffusion, DALL-E y MidJourney.\u00a0<\/span><\/p>\n

En su experimento<\/a>El equipo entren\u00f3 a las IA con im\u00e1genes reales o generadas por ellas. 70.000 rostros humanos reales extra\u00eddos de Flickr.<\/span><\/p>\n

Cuando cada IA se entren\u00f3 con sus propias im\u00e1genes generadas por la IA, los resultados del generador de im\u00e1genes StyleGAN empezaron a mostrar patrones visuales distorsionados y ondulados, mientras que los resultados del generador de im\u00e1genes de difusi\u00f3n se volvieron m\u00e1s borrosos.<\/span><\/p>\n

En ambos casos, el entrenamiento de las IAs en im\u00e1genes generadas por ellas supuso una p\u00e9rdida de calidad.\u00a0<\/span><\/p>\n

Uno de los <\/span>estudiar<\/span> autores, Richard Baraniuk, de la Universidad Rice de Texas, advierte: \"Va a haber una pendiente resbaladiza hacia el uso de datos sint\u00e9ticos, ya sea a sabiendas o sin querer\".<\/span><\/p>\n

\"Generador
Cuanto m\u00e1s se entrenan los modelos de IA con im\u00e1genes sint\u00e9ticas, m\u00e1s borrosos son sus resultados. Fuente: Universidad de Rice<\/a>.<\/figcaption><\/figure>\n

Aunque la disminuci\u00f3n de la calidad de las im\u00e1genes se redujo seleccionando para el entrenamiento im\u00e1genes de mayor calidad generadas por la IA, esto provoc\u00f3 una p\u00e9rdida de diversidad de im\u00e1genes.\u00a0<\/span><\/p>\n

Los investigadores tambi\u00e9n probaron a incorporar un conjunto fijo de im\u00e1genes reales a conjuntos de entrenamiento que inclu\u00edan principalmente im\u00e1genes generadas por IA, un m\u00e9todo que a veces se utiliza para complementar conjuntos de entrenamiento peque\u00f1os.\u00a0<\/span><\/p>\n

Sin embargo, esto s\u00f3lo retras\u00f3 el declive de la calidad de la imagen: parece inevitable que cuantos m\u00e1s datos generados por la IA entren en los conjuntos de datos de entrenamiento, peor ser\u00e1 el resultado. S\u00f3lo es cuesti\u00f3n de tiempo.<\/span><\/p>\n

Se obtuvieron resultados razonables cuando cada IA se entren\u00f3 con una mezcla de im\u00e1genes generadas por la IA y un conjunto de im\u00e1genes aut\u00e9nticas en constante cambio. Esto ayud\u00f3 a mantener la calidad y diversidad de las im\u00e1genes.<\/span><\/p>\n

Es dif\u00edcil equilibrar cantidad y calidad: las im\u00e1genes sint\u00e9ticas son potencialmente ilimitadas en comparaci\u00f3n con las reales, pero su uso tiene un coste.\u00a0<\/span><\/p>\n

Las IA se quedan sin datos<\/span><\/h2>\n

Las IA est\u00e1n \u00e1vidas de datos, pero los datos aut\u00e9nticos y de alta calidad son un recurso finito.\u00a0<\/span><\/p>\n

Los resultados de esta investigaci\u00f3n se hacen eco <\/span>estudios similares para la generaci\u00f3n de textos<\/span><\/a>donde los resultados de la IA tienden a resentirse cuando los modelos se entrenan con texto generado por la IA.\u00a0<\/span><\/p>\n

Los investigadores destacan que las organizaciones m\u00e1s peque\u00f1as con capacidad limitada para recopilar datos aut\u00e9nticos son las que se enfrentan a mayores dificultades para filtrar las im\u00e1genes generadas por IA de sus conjuntos de datos.\u00a0<\/span><\/p>\n

Adem\u00e1s, el problema se agrava porque Internet se est\u00e1 inundando de contenidos generados por IA, lo que dificulta enormemente determinar el tipo de datos con los que se entrenan los modelos.\u00a0<\/span><\/p>\n

Sina Alemohammad, de la Universidad Rice, sugiere que el desarrollo de marcas de agua para identificar las im\u00e1genes generadas por IA podr\u00eda ayudar, pero advierte de que pasar por alto las marcas de agua ocultas puede degradar la calidad de las im\u00e1genes generadas por IA.<\/span><\/p>\n

Alemohammad concluye: \"Est\u00e1s condenado si lo haces y condenado si no lo haces. Pero sin duda es mejor poner una marca de agua en la imagen que no hacerlo\".<\/span><\/p>\n

Las consecuencias a largo plazo del consumo de los resultados de la IA son objeto de acalorados debates, pero ahora mismo, los desarrolladores de IA necesitan encontrar soluciones para garantizar la calidad de sus modelos. <\/span><\/p>","protected":false},"excerpt":{"rendered":"

Un nuevo estudio demuestra que el entrenamiento de los generadores de im\u00e1genes de IA con im\u00e1genes generadas por la propia IA acaba provocando una reducci\u00f3n significativa de la calidad del resultado. Baraniuk y su equipo demostraron c\u00f3mo este problem\u00e1tico bucle de entrenamiento de IA afecta a las IA generativas, incluidos los modelos StyleGAN y de difusi\u00f3n. Estos modelos son algunos de los que se utilizan para generar im\u00e1genes de IA, como Stable Diffusion, DALL-E y MidJourney. En su experimento, el equipo entren\u00f3 a las IA con im\u00e1genes reales o generadas por ellas. 70.000 rostros humanos reales extra\u00eddos de Flickr. Cuando cada IA se entren\u00f3 con sus propias im\u00e1genes generadas por la IA, los resultados del generador de im\u00e1genes StyleGAN empezaron a mostrar patrones visuales distorsionados y ondulados,<\/p>","protected":false},"author":2,"featured_media":2682,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[233,234,174],"class_list":["post-2680","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-image-generator","tag-midjourney","tag-stable-diffusion"],"yoast_head":"\nAI image generators' quality suffers when trained on AI-generated data | DailyAI<\/title>\n<meta name=\"description\" content=\"A new study shows that training AI image generators with AI-generated images could lead to a self-consuming loop.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link 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