{"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\/nl\/2024\/05\/researchers-train-model-to-create-images-without-seeing-copyrighted-work\/","title":{"rendered":"Onderzoekers trainen model om afbeeldingen te maken zonder auteursrechtelijk beschermd werk te 'zien'"},"content":{"rendered":"

Onderzoekers van de Universiteit van Texas in Austin hebben een innovatief raamwerk ontwikkeld voor het trainen van AI-modellen op zwaar beschadigde afbeeldingen.\u00a0<\/b><\/p>\n

Deze methode, bekend als Ambient Diffusion, stelt AI-modellen in staat om 'inspiratie op te doen' uit afbeeldingen zonder<\/em> ze rechtstreeks kopi\u00ebren.<\/span><\/p>\n

Conventionele tekst-naar-beeld modellen gebruikt door DALL-E<\/span><\/span><\/span>, Midjourney en Stable Diffusion lopen het risico op schending van auteursrechten omdat ze zijn getraind op datasets die afbeeldingen bevatten waarop auteursrechten rusten, waardoor ze deze afbeeldingen soms onbedoeld kopi\u00ebren.\u00a0<\/span><\/p>\n

Ambient Diffusion draait dat om door modellen te trainen met opzettelijk corrupte gegevens.<\/p>\n

In de onderzoek<\/a>Het onderzoeksteam, waaronder Alex Dimakis en Giannis Daras van de afdeling Electrical and Computer Engineering van UT Austin en Constantinos Daskalakis van MIT<\/span><\/span><\/span><\/span><\/span>trainde een Stable Diffusion XL-model op een dataset van 3.000 afbeeldingen van beroemdheden.\u00a0<\/span><\/p>\n

In eerste instantie werd duidelijk waargenomen dat de modellen die op schone gegevens waren getraind, de trainingsvoorbeelden kopieerden.\u00a0<\/span><\/p>\n

Toen de trainingsgegevens echter corrupt waren - waarbij willekeurig tot 90% van de pixels werd gemaskeerd - produceerde het model nog steeds unieke afbeeldingen van hoge kwaliteit.<\/span><\/p>\n

Dit betekent dat de AI nooit wordt blootgesteld aan herkenbare versies van de originele afbeeldingen, waardoor hij ze niet kan kopi\u00ebren.<\/span><\/p>\n

\"Ons raamwerk maakt het mogelijk om de afweging tussen memoriseren en presteren te controleren,\" aldus de directeur. <\/span>verklaarde Giannis Daras<\/span><\/a>, een afgestudeerde student computerwetenschappen die het werk leidde.\u00a0<\/span><\/p>\n

\"Naarmate de mate van corruptie tijdens de training toeneemt, neemt de memorisatie van de trainingsset af.\"<\/span><\/p>\n

Wetenschappelijke en medische toepassingen<\/h2>\n

Het gebruik van Ambient Diffusion gaat verder dan het oplossen van auteursrechtenkwesties.\u00a0<\/span><\/p>\n

Volgens professor Adam Klivans, een van de medewerkers aan het project, \"zou het raamwerk ook nuttig kunnen zijn voor wetenschappelijke en medische toepassingen. Dat geldt in principe voor elk onderzoek waarbij het duur of onmogelijk is om een volledige set van ongecorrumpeerde gegevens te hebben, van beeldvorming van zwarte gaten tot bepaalde soorten MRI-scans.\"<\/span><\/p>\n

Dit is vooral nuttig op gebieden met beperkte toegang tot onbeschadigde gegevens, zoals astronomie en deeltjesfysica<\/a>.\u00a0<\/span><\/p>\n

In deze en andere vakgebieden kunnen gegevens extreem ruisachtig, van slechte kwaliteit of schaars zijn, waardoor zinvolle gegevens sterk in de minderheid zijn ten opzichte van nutteloze gegevens. Modellen aanleren om suboptimale gegevens effici\u00ebnter te gebruiken zou hier nuttig zijn.<\/p>\n

Als de Ambient Diffusion-benadering verder verfijnd zou worden, zouden AI-bedrijven functionele tekst-naar-beeld-modellen kunnen maken en tegelijkertijd de rechten van de oorspronkelijke makers van de inhoud kunnen respecteren en juridische problemen kunnen voorkomen.<\/p>\n

Hoewel dit de bezorgdheid dat AI-afbeeldingsprogramma's de hoeveelheid werk voor echte artiesten verminderen niet zou wegnemen, zou het op zijn minst hun werk beschermen tegen het per ongeluk kopi\u00ebren in outputs.<\/p>","protected":false},"excerpt":{"rendered":"

Onderzoekers van de Universiteit van Texas in Austin hebben een innovatief raamwerk ontwikkeld voor het trainen van AI-modellen op zwaar beschadigde afbeeldingen. Deze methode, bekend onder de naam Ambient Diffusion, stelt AI-modellen in staat om 'inspiratie op te doen' uit afbeeldingen zonder deze direct te kopi\u00ebren. Conventionele tekst-naar-beeld modellen die worden gebruikt door DALL-E, Midjourney en Stable Diffusion lopen het risico auteursrecht te schenden omdat ze worden getraind op datasets met auteursrechtelijk beschermde afbeeldingen, waardoor ze deze afbeeldingen soms onbedoeld kopi\u00ebren. Ambient Diffusion draait dit om door modellen te trainen met opzettelijk beschadigde gegevens. In het onderzoek heeft het onderzoeksteam, waaronder Alex Dimakis en Giannis Daras van de afdeling 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\/nl\/2024\/05\/researchers-train-model-to-create-images-without-seeing-copyrighted-work\/\" \/>\n<meta property=\"og:locale\" content=\"nl_NL\" \/>\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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