{"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\/sv\/2024\/05\/researchers-train-model-to-create-images-without-seeing-copyrighted-work\/","title":{"rendered":"Forskare tr\u00e4nar modell att skapa bilder utan att \"se\" upphovsr\u00e4ttsskyddat arbete"},"content":{"rendered":"

Forskare vid University of Texas i Austin har utvecklat ett innovativt ramverk f\u00f6r att tr\u00e4na AI-modeller p\u00e5 kraftigt korrumperade bilder.\u00a0<\/b><\/p>\n

Metoden, som kallas Ambient Diffusion, g\u00f6r det m\u00f6jligt f\u00f6r AI-modeller att \"h\u00e4mta inspiration\" fr\u00e5n bilder utan<\/em> direkt kopiera dem.<\/span><\/p>\n

Konventionella text-till-bild-modeller som anv\u00e4nds av DALL-E<\/span><\/span><\/span>, Midjourney och Stable Diffusion riskerar upphovsr\u00e4ttsintr\u00e5ng eftersom de tr\u00e4nas p\u00e5 dataset som inneh\u00e5ller upphovsr\u00e4ttsskyddade bilder, vilket leder till att de ibland oavsiktligt kopierar dessa bilder.\u00a0<\/span><\/p>\n

Ambient Diffusion v\u00e4nder upp och ner p\u00e5 detta genom att tr\u00e4na modeller med avsiktligt korrumperad data.<\/p>\n

I studie<\/a>Forskarteamet, som best\u00e5r av Alex Dimakis och Giannis Daras fr\u00e5n avdelningen f\u00f6r elektro- och datateknik vid UT Austin och Constantinos Daskalakis fr\u00e5n MIT<\/span><\/span><\/span><\/span><\/span>tr\u00e4nade en Stable Diffusion XL-modell p\u00e5 ett dataset med 3.000 k\u00e4ndisbilder.\u00a0<\/span><\/p>\n

Till en b\u00f6rjan observerades att modeller som tr\u00e4nats p\u00e5 rena data helt uppenbart kopierade tr\u00e4ningsexemplen.\u00a0<\/span><\/p>\n

Men n\u00e4r tr\u00e4ningsdata korrumperades - upp till 90% av pixlarna maskerades slumpm\u00e4ssigt - producerade modellen fortfarande unika bilder av h\u00f6g kvalitet.<\/span><\/p>\n

Det inneb\u00e4r att AI:n aldrig exponeras f\u00f6r igenk\u00e4nnbara versioner av originalbilderna, vilket hindrar den fr\u00e5n att kopiera dem.<\/span><\/p>\n

\"V\u00e5rt ramverk g\u00f6r det m\u00f6jligt att kontrollera avv\u00e4gningen mellan memorering och prestanda,\" <\/span>f\u00f6rklarade Giannis Daras<\/span><\/a>, en doktorand i datavetenskap som ledde arbetet.\u00a0<\/span><\/p>\n

\"I takt med att graden av korruption under tr\u00e4ningen \u00f6kar, minskar inl\u00e4rningen av tr\u00e4ningsmaterialet.\"<\/span><\/p>\n

Vetenskapliga och medicinska till\u00e4mpningar<\/h2>\n

Anv\u00e4ndningen av Ambient Diffusion str\u00e4cker sig l\u00e4ngre \u00e4n till att l\u00f6sa upphovsr\u00e4ttsliga problem.\u00a0<\/span><\/p>\n

Enligt professor Adam Klivans, en av projektets medarbetare, \"kan ramverket visa sig vara anv\u00e4ndbart \u00e4ven f\u00f6r vetenskapliga och medicinska till\u00e4mpningar. Det skulle g\u00e4lla i princip all forskning d\u00e4r det \u00e4r dyrt eller om\u00f6jligt att ha en fullst\u00e4ndig upps\u00e4ttning okorrumperade data, fr\u00e5n avbildning av svarta h\u00e5l till vissa typer av magnetkameraunders\u00f6kningar.\"<\/span><\/p>\n

Detta \u00e4r s\u00e4rskilt f\u00f6rdelaktigt inom omr\u00e5den med begr\u00e4nsad tillg\u00e5ng till okorrumperad data, t.ex. astronomi och partikelfysik<\/a>.\u00a0<\/span><\/p>\n

Inom dessa och andra omr\u00e5den kan data vara extremt brusiga, av d\u00e5lig kvalitet eller glesa, vilket inneb\u00e4r att meningsfulla data \u00e4r kraftigt \u00f6verdrivna av v\u00e4rdel\u00f6sa data. Att l\u00e4ra modeller att anv\u00e4nda suboptimala data mer effektivt skulle vara till hj\u00e4lp h\u00e4r.<\/p>\n

Om Ambient Diffusion-metoden f\u00f6rfinades ytterligare skulle AI-f\u00f6retag kunna skapa funktionella text-till-bild-modeller samtidigt som de respekterar r\u00e4ttigheterna f\u00f6r skaparna av originalinneh\u00e5llet och f\u00f6rebygger juridiska problem.<\/p>\n

\u00c4ven om det inte skulle l\u00f6sa farh\u00e5gorna om att AI-bildverktyg minskar utbudet av arbete f\u00f6r riktiga konstn\u00e4rer, skulle det \u00e5tminstone skydda deras verk fr\u00e5n att av misstag replikeras i utdata.<\/p>","protected":false},"excerpt":{"rendered":"

Forskare vid University of Texas i Austin har utvecklat ett innovativt ramverk f\u00f6r att tr\u00e4na AI-modeller p\u00e5 kraftigt korrumperade bilder. Metoden, som kallas Ambient Diffusion, g\u00f6r det m\u00f6jligt f\u00f6r AI-modeller att \"h\u00e4mta inspiration\" fr\u00e5n bilder utan att direkt kopiera dem. Konventionella text-till-bild-modeller som anv\u00e4nds av DALL-E, Midjourney och Stable Diffusion riskerar upphovsr\u00e4ttsintr\u00e5ng eftersom de tr\u00e4nas p\u00e5 dataset som inneh\u00e5ller upphovsr\u00e4ttsskyddade bilder, vilket leder till att de ibland oavsiktligt replikerar dessa bilder. Ambient Diffusion v\u00e4nder det p\u00e5 huvudet genom att tr\u00e4na modeller med avsiktligt skadade data. I studien har forskargruppen, som bland annat best\u00e5r av Alex Dimakis och Giannis Daras fr\u00e5n 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\/sv\/2024\/05\/researchers-train-model-to-create-images-without-seeing-copyrighted-work\/\" \/>\n<meta property=\"og:locale\" content=\"sv_SE\" \/>\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\/sv\/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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