{"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\/da\/2024\/05\/researchers-train-model-to-create-images-without-seeing-copyrighted-work\/","title":{"rendered":"Forskere tr\u00e6ner model til at skabe billeder uden at 'se' ophavsretligt beskyttet arbejde"},"content":{"rendered":"

Forskere ved University of Texas i Austin har udviklet en innovativ ramme til at tr\u00e6ne AI-modeller p\u00e5 st\u00e6rkt beskadigede billeder.\u00a0<\/b><\/p>\n

Metoden er kendt som Ambient Diffusion og g\u00f8r det muligt for AI-modeller at \"hente inspiration\" fra billeder. uden<\/em> direkte kopiering af dem.<\/span><\/p>\n

Konventionelle tekst-til-billede-modeller, der bruges af DALL-E<\/span><\/span><\/span>, Midjourney og Stable Diffusion risikerer at kr\u00e6nke ophavsretten, fordi de er tr\u00e6net p\u00e5 datas\u00e6t, der indeholder ophavsretligt beskyttede billeder, hvilket f\u00f8rer til, at de nogle gange utilsigtet kopierer disse billeder.\u00a0<\/span><\/p>\n

Ambient Diffusion vender det p\u00e5 hovedet ved at tr\u00e6ne modeller med bevidst korrumperede data.<\/p>\n

I unders\u00f8gelse<\/a>Forskerteamet, der omfatter Alex Dimakis og Giannis Daras fra afdelingen for elektro- og computerteknik p\u00e5 UT Austin og Constantinos Daskalakis fra MIT<\/span><\/span><\/span><\/span><\/span>tr\u00e6nede en Stable Diffusion XL-model p\u00e5 et datas\u00e6t med 3.000 billeder af ber\u00f8mtheder.\u00a0<\/span><\/p>\n

Til at begynde med blev modellerne, der var tr\u00e6net p\u00e5 rene data, observeret som \u00e5benlyse kopier af tr\u00e6ningseksemplerne.\u00a0<\/span><\/p>\n

Men da tr\u00e6ningsdataene blev \u00f8delagt - tilf\u00e6ldigt maskeret op til 90% af pixels - producerede modellen stadig unikke billeder af h\u00f8j kvalitet.<\/span><\/p>\n

Det betyder, at AI'en aldrig uds\u00e6ttes for genkendelige versioner af de originale billeder, hvilket forhindrer den i at kopiere dem.<\/span><\/p>\n

\"Vores rammer g\u00f8r det muligt at kontrollere afvejningen mellem hukommelse og pr\u00e6station,\" <\/span>forklarede Giannis Daras<\/span><\/a>en kandidatstuderende i datalogi, som ledede arbejdet.\u00a0<\/span><\/p>\n

\"I takt med at graden af korruption under tr\u00e6ningen \u00f8ges, mindskes indl\u00e6ringen af tr\u00e6ningss\u00e6ttet.\"<\/span><\/p>\n

Videnskabelige og medicinske anvendelser<\/h2>\n

Ambient Diffusion kan bruges til meget mere end at l\u00f8se problemer med ophavsret.\u00a0<\/span><\/p>\n

If\u00f8lge professor Adam Klivans, en af projektets samarbejdspartnere, \"kan rammerne ogs\u00e5 vise sig at v\u00e6re nyttige til videnskabelige og medicinske anvendelser. Det g\u00e6lder for stort set al forskning, hvor det er dyrt eller umuligt at have et komplet s\u00e6t ukorrumperede data, fra billeddannelse af sorte huller til visse typer MR-scanninger.\"<\/span><\/p>\n

Det er is\u00e6r en fordel inden for omr\u00e5der med begr\u00e6nset adgang til ukorrumperede data, som f.eks. astronomi og partikelfysik<\/a>.\u00a0<\/span><\/p>\n

P\u00e5 disse og andre omr\u00e5der kan data v\u00e6re ekstremt st\u00f8jende, af d\u00e5rlig kvalitet eller sparsomme, hvilket betyder, at meningsfulde data er i st\u00e6rkt undertal i forhold til ubrugelige data. Her ville det v\u00e6re nyttigt at l\u00e6re modeller at bruge suboptimale data mere effektivt.<\/p>\n

Hvis Ambient Diffusion-tilgangen blev yderligere forfinet, kunne AI-virksomheder skabe funktionelle tekst-til-billede-modeller, samtidig med at de respekterede de oprindelige indholdsskaberes rettigheder og forhindrede juridiske problemer.<\/p>\n

Selv om det ikke ville l\u00f8se problemerne med, at AI-billedv\u00e6rkt\u00f8jer reducerer m\u00e6ngden af arbejde for rigtige kunstnere, ville det i det mindste beskytte deres v\u00e6rker mod at blive kopieret i outputs ved et uheld.<\/p>","protected":false},"excerpt":{"rendered":"

Forskere ved University of Texas i Austin har udviklet en innovativ ramme for tr\u00e6ning af AI-modeller p\u00e5 st\u00e6rkt beskadigede billeder. Metoden er kendt som Ambient Diffusion og g\u00f8r det muligt for AI-modeller at \"hente inspiration\" fra billeder uden at kopiere dem direkte. Konventionelle tekst-til-billede-modeller, der bruges af DALL-E, Midjourney og Stable Diffusion, risikerer at kr\u00e6nke ophavsretten, fordi de tr\u00e6nes p\u00e5 datas\u00e6t, der indeholder ophavsretligt beskyttede billeder, hvilket f\u00f8rer til, at de nogle gange utilsigtet kopierer disse billeder. Ambient Diffusion vender det p\u00e5 hovedet ved at tr\u00e6ne modeller med bevidst \u00f8delagte data. I unders\u00f8gelsen har forskerholdet, herunder Alex Dimakis og Giannis Daras fra Electrical and Computer<\/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\/da\/2024\/05\/researchers-train-model-to-create-images-without-seeing-copyrighted-work\/\" \/>\n<meta property=\"og:locale\" content=\"da_DK\" \/>\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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In the study, the research team, including Alex Dimakis and Giannis Daras from the Electrical and Computer\" \/>\n<meta property=\"og:url\" content=\"https:\/\/dailyai.com\/da\/2024\/05\/researchers-train-model-to-create-images-without-seeing-copyrighted-work\/\" \/>\n<meta property=\"og:site_name\" content=\"DailyAI\" \/>\n<meta property=\"article:published_time\" content=\"2024-05-21T18:26:24+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2024-05-21T20:56:24+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/dailyai.com\/wp-content\/uploads\/2024\/05\/DALL\u00b7E-2024-05-21-19.21.33-A-high-tech-lab-with-researchers-working-on-a-diffusion-model-for-creating-images-without-seeing-copyrighted-work.-The-focal-point-is-a-large-comput.webp\" \/>\n\t<meta property=\"og:image:width\" content=\"1792\" \/>\n\t<meta property=\"og:image:height\" content=\"1024\" \/>\n\t<meta property=\"og:image:type\" content=\"image\/webp\" \/>\n<meta name=\"author\" content=\"Sam 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