{"id":12486,"date":"2024-05-21T18:26:24","date_gmt":"2024-05-21T18:26:24","guid":{"rendered":"https:\/\/dailyai.com\/?p=12486"},"modified":"2024-05-21T20:56:24","modified_gmt":"2024-05-21T20:56:24","slug":"researchers-train-model-to-create-images-without-seeing-copyrighted-work","status":"publish","type":"post","link":"https:\/\/dailyai.com\/es\/2024\/05\/researchers-train-model-to-create-images-without-seeing-copyrighted-work\/","title":{"rendered":"Investigadores entrenan a un modelo para crear im\u00e1genes sin \"ver\" obras protegidas por derechos de autor"},"content":{"rendered":"

Investigadores de la Universidad de Texas en Austin han desarrollado un innovador marco para entrenar modelos de IA en im\u00e1genes muy corruptas.\u00a0<\/b><\/p>\n

Conocido como Ambient Diffusion, este m\u00e9todo permite a los modelos de IA \"inspirarse\" en im\u00e1genes sin<\/em> copi\u00e1ndolos directamente.<\/span><\/p>\n

Los modelos convencionales de conversi\u00f3n de texto en imagen utilizados por DALL-E<\/span><\/span><\/span>Midjourney y Stable Diffusion corren el riesgo de infringir los derechos de autor porque est\u00e1n entrenados con conjuntos de datos que incluyen im\u00e1genes protegidas por derechos de autor, lo que les lleva a veces a replicar inadvertidamente esas im\u00e1genes.\u00a0<\/span><\/p>\n

Ambient Diffusion le da la vuelta a la tortilla entrenando modelos con datos deliberadamente corruptos.<\/p>\n

En el estudiar<\/a>El equipo de investigaci\u00f3n, formado por Alex Dimakis y Giannis Daras, del departamento de Ingenier\u00eda El\u00e9ctrica e Inform\u00e1tica de UT Austin, y Constantinos Daskalakis, de MIT<\/span><\/span><\/span><\/span><\/span>entren\u00f3 un modelo Stable Diffusion XL en un conjunto de datos de 3.000 im\u00e1genes de famosos.\u00a0<\/span><\/p>\n

Al principio, se observ\u00f3 que los modelos entrenados con datos limpios copiaban descaradamente los ejemplos de entrenamiento.\u00a0<\/span><\/p>\n

Sin embargo, cuando los datos de entrenamiento se corromp\u00edan -enmascarando aleatoriamente hasta 90% de los p\u00edxeles-, el modelo segu\u00eda produciendo im\u00e1genes \u00fanicas de alta calidad.<\/span><\/p>\n

Esto significa que la IA nunca est\u00e1 expuesta a versiones reconocibles de las im\u00e1genes originales, lo que le impide copiarlas.<\/span><\/p>\n

\"Nuestro marco permite controlar el equilibrio entre memorizaci\u00f3n y rendimiento\". <\/span>explica Giannis Daras<\/span><\/a>estudiante de inform\u00e1tica que dirigi\u00f3 el trabajo.\u00a0<\/span><\/p>\n

\"A medida que aumenta el nivel de corrupci\u00f3n encontrado durante el entrenamiento, disminuye la memorizaci\u00f3n del conjunto de entrenamiento\".<\/span><\/p>\n

Aplicaciones cient\u00edficas y m\u00e9dicas<\/h2>\n

Los usos de Ambient Diffusion van m\u00e1s all\u00e1 de la resoluci\u00f3n de problemas de derechos de autor.\u00a0<\/span><\/p>\n

Seg\u00fan el profesor Adam Klivans, colaborador del proyecto, \"el marco podr\u00eda resultar \u00fatil tambi\u00e9n para aplicaciones cient\u00edficas y m\u00e9dicas. Esto ser\u00eda v\u00e1lido b\u00e1sicamente para cualquier investigaci\u00f3n en la que resulte caro o imposible disponer de un conjunto completo de datos no corrompidos, desde la obtenci\u00f3n de im\u00e1genes de agujeros negros hasta ciertos tipos de resonancias magn\u00e9ticas.\"<\/span><\/p>\n

Esto es especialmente beneficioso en campos con acceso limitado a datos no corruptos, como por ejemplo astronom\u00eda y f\u00edsica de part\u00edculas<\/a>.\u00a0<\/span><\/p>\n

En estos campos y en otros, los datos pueden ser extremadamente ruidosos, de mala calidad o escasos, de modo que los datos significativos se ven superados en n\u00famero por los datos in\u00fatiles. Ense\u00f1ar a los modelos a utilizar los datos sub\u00f3ptimos de forma m\u00e1s eficiente ser\u00eda \u00fatil en este caso.<\/p>\n

Si se perfeccionara el enfoque de la difusi\u00f3n ambiental, las empresas de IA podr\u00edan crear modelos funcionales de texto a imagen respetando los derechos de los creadores de contenidos originales y evitando problemas legales.<\/p>\n

Aunque eso no solucionar\u00eda el problema de que las herramientas de imagen por IA reduzcan la oferta de trabajo de los verdaderos artistas, al menos proteger\u00eda sus obras de ser reproducidas accidentalmente en productos.<\/p>","protected":false},"excerpt":{"rendered":"

Investigadores de la Universidad de Texas en Austin han desarrollado un innovador marco para entrenar modelos de inteligencia artificial a partir de im\u00e1genes muy corruptas. Conocido como Ambient Diffusion, este m\u00e9todo permite a los modelos de IA \"inspirarse\" en im\u00e1genes sin copiarlas directamente. Los modelos convencionales de conversi\u00f3n de texto en im\u00e1genes utilizados por DALL-E, Midjourney y Stable Diffusion corren el riesgo de infringir los derechos de autor porque se entrenan en conjuntos de datos que incluyen im\u00e1genes protegidas por derechos de autor, lo que a veces les lleva a replicarlas sin darse cuenta. Ambient Diffusion le da la vuelta a la tortilla entrenando modelos con datos deliberadamente corruptos. En el estudio, el equipo de investigaci\u00f3n, formado por Alex Dimakis y Giannis Daras, de la Facultad de Ingenier\u00eda El\u00e9ctrica e Inform\u00e1tica de la Universidad de Harvard, estudia el uso de datos corruptos en el entrenamiento de modelos.<\/p>","protected":false},"author":2,"featured_media":12492,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[88],"tags":[165,105],"class_list":["post-12486","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ethics","tag-image-generation","tag-machine-learning"],"yoast_head":"\nResearchers train model to create images without 'seeing' copyrighted work | 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\/05\/researchers-train-model-to-create-images-without-seeing-copyrighted-work\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Researchers train model to create images without 'seeing' copyrighted work | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Researchers at The University of Texas at Austin have developed an innovative framework for training AI models on heavily corrupted images.\u00a0 Known as Ambient Diffusion, this method enables AI models to ‘draw inspiration’ from images without directly copying them. 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