{"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\/nb\/2024\/05\/researchers-train-model-to-create-images-without-seeing-copyrighted-work\/","title":{"rendered":"Forskere trener opp en modell til \u00e5 skape bilder uten \u00e5 \"se\" opphavsrettsbeskyttet arbeid"},"content":{"rendered":"

Forskere ved University of Texas i Austin har utviklet et innovativt rammeverk for \u00e5 trene opp AI-modeller p\u00e5 bilder som er sterkt korrupte.\u00a0<\/b><\/p>\n

Metoden kalles Ambient Diffusion, og gj\u00f8r det mulig for AI-modeller \u00e5 \"hente inspirasjon\" fra bilder uten<\/em> direkte kopiering av dem.<\/span><\/p>\n

Konvensjonelle tekst-til-bilde-modeller som brukes av DALL-E<\/span><\/span><\/span>, Midjourney og Stable Diffusion risikerer brudd p\u00e5 opphavsretten fordi de er oppl\u00e6rt p\u00e5 datasett som inneholder opphavsrettsbeskyttede bilder, noe som f\u00f8rer til at de noen ganger utilsiktet kopierer disse bildene.\u00a0<\/span><\/p>\n

Ambient Diffusion snur dette p\u00e5 hodet ved \u00e5 trene opp modeller med bevisst korrupte data.<\/p>\n

I studie<\/a>Forskerteamet, som best\u00e5r av Alex Dimakis og Giannis Daras fra Electrical and Computer Engineering-avdelingen ved UT Austin og Constantinos Daskalakis fra MIT<\/span><\/span><\/span><\/span><\/span>trente en Stable Diffusion XL-modell p\u00e5 et datasett med 3000 kjendisbilder.\u00a0<\/span><\/p>\n

I utgangspunktet ble det observert at modellene som var trent opp p\u00e5 rene data, \u00e5penbart kopierte treningseksemplene.\u00a0<\/span><\/p>\n

Men n\u00e5r treningsdataene ble \u00f8delagt - opp til 90% av pikslene ble tilfeldig maskert - produserte modellen likevel unike bilder av h\u00f8y kvalitet.<\/span><\/p>\n

Dette betyr at den kunstige intelligensen aldri blir eksponert for gjenkjennelige versjoner av originalbildene, slik at den ikke kan kopiere dem.<\/span><\/p>\n

\"Rammeverket v\u00e5rt gj\u00f8r det mulig \u00e5 kontrollere avveiningen mellom memorering og ytelse,\" <\/span>forklarte Giannis Daras<\/span><\/a>, en doktorgradsstudent i informatikk som ledet arbeidet.\u00a0<\/span><\/p>\n

\"Etter hvert som korrupsjonsniv\u00e5et som oppst\u00e5r under oppl\u00e6ringen \u00f8ker, reduseres memoreringen av oppl\u00e6ringssettet.\"<\/span><\/p>\n

Vitenskapelige og medisinske bruksomr\u00e5der<\/h2>\n

Ambient Diffusion kan brukes til mer enn \u00e5 l\u00f8se opphavsrettslige problemer.\u00a0<\/span><\/p>\n

If\u00f8lge professor Adam Klivans, en av medarbeiderne i prosjektet, kan rammeverket vise seg \u00e5 v\u00e6re nyttig ogs\u00e5 for vitenskapelige og medisinske anvendelser. Det gjelder i prinsippet all forskning der det er dyrt eller umulig \u00e5 ha et fullstendig sett med ukorrupte data, fra avbildning av sorte hull til visse typer MR-skanninger.\"<\/span><\/p>\n

Dette er spesielt fordelaktig p\u00e5 omr\u00e5der med begrenset tilgang til ukorrupte data, som for eksempel astronomi og partikkelfysikk<\/a>.\u00a0<\/span><\/p>\n

P\u00e5 disse og andre felt kan dataene v\u00e6re ekstremt st\u00f8yende, av d\u00e5rlig kvalitet eller sparsomme, slik at meningsfulle data er i stort mindretall i forhold til ubrukelige data. Her kan det v\u00e6re nyttig \u00e5 l\u00e6re modeller \u00e5 bruke suboptimale data mer effektivt.<\/p>\n

Hvis Ambient Diffusion-tiln\u00e6rmingen ble videreutviklet, kunne AI-selskaper lage funksjonelle tekst-til-bilde-modeller samtidig som de respekterer rettighetene til de opprinnelige innholdsskaperne og unng\u00e5r juridiske problemer.<\/p>\n

Selv om det ikke ville l\u00f8se bekymringene for at AI-bildeverkt\u00f8y reduserer arbeidstilbudet for ekte kunstnere, ville det i det minste beskytte verkene deres mot \u00e5 bli replikert i utdata ved et uhell.<\/p>","protected":false},"excerpt":{"rendered":"

Forskere ved University of Texas i Austin har utviklet et innovativt rammeverk for \u00e5 trene opp AI-modeller p\u00e5 bilder som er sterkt korrupte. Metoden kalles Ambient Diffusion og gj\u00f8r det mulig for AI-modeller \u00e5 \"hente inspirasjon\" fra bilder uten \u00e5 kopiere dem direkte. Konvensjonelle tekst-til-bilde-modeller som brukes av DALL-E, Midjourney og Stable Diffusion, risikerer \u00e5 krenke opphavsretten fordi de er trent p\u00e5 datasett som inneholder opphavsrettsbeskyttede bilder, noe som f\u00f8rer til at de noen ganger utilsiktet kopierer disse bildene. Ambient Diffusion snur dette p\u00e5 hodet ved \u00e5 trene modeller med bevisst korrupte data. I studien har forskerteamet, inkludert 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\/nb\/2024\/05\/researchers-train-model-to-create-images-without-seeing-copyrighted-work\/\" \/>\n<meta property=\"og:locale\" content=\"nb_NO\" \/>\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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