{"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\/de\/2024\/05\/researchers-train-model-to-create-images-without-seeing-copyrighted-work\/","title":{"rendered":"Forscher trainieren Modell, um Bilder zu erstellen, ohne urheberrechtlich gesch\u00fctzte Werke zu \"sehen"},"content":{"rendered":"

Forscher der University of Texas in Austin haben einen innovativen Rahmen f\u00fcr das Training von KI-Modellen auf stark besch\u00e4digten Bildern entwickelt.\u00a0<\/b><\/p>\n

Diese als Ambient Diffusion bekannte Methode erm\u00f6glicht es KI-Modellen, sich von Bildern \"inspirieren\" zu lassen ohne<\/em> sie direkt zu kopieren.<\/span><\/p>\n

Herk\u00f6mmliche Text-zu-Bild-Modelle, die von DALL-E<\/span><\/span><\/span>, Midjourney und Stable Diffusion riskieren Urheberrechtsverletzungen, weil sie auf Datens\u00e4tzen trainiert werden, die urheberrechtlich gesch\u00fctzte Bilder enthalten, was dazu f\u00fchrt, dass sie diese Bilder manchmal versehentlich reproduzieren.\u00a0<\/span><\/p>\n

Ambient Diffusion stellt dies auf den Kopf, indem es Modelle mit absichtlich verf\u00e4lschten Daten trainiert.<\/p>\n

In der Studie<\/a>Das Forschungsteam, dem Alex Dimakis und Giannis Daras von der Abteilung f\u00fcr Elektro- und Computertechnik der UT Austin und Constantinos Daskalakis von der MIT<\/span><\/span><\/span><\/span><\/span>trainierte ein Stable Diffusion XL-Modell auf einem Datensatz von 3.000 Prominentenbildern.\u00a0<\/span><\/p>\n

Zun\u00e4chst wurde beobachtet, dass die Modelle, die auf sauberen Daten trainiert wurden, ganz offensichtlich die Trainingsbeispiele kopierten.\u00a0<\/span><\/p>\n

Wurden die Trainingsdaten jedoch verf\u00e4lscht, indem bis zu 90% der Pixel zuf\u00e4llig maskiert wurden, erzeugte das Modell immer noch hochwertige, einzigartige Bilder.<\/span><\/p>\n

Das bedeutet, dass die KI niemals mit erkennbaren Versionen der Originalbilder in Ber\u00fchrung kommt, was sie daran hindert, diese zu kopieren.<\/span><\/p>\n

\"Unser Rahmen erm\u00f6glicht es, den Kompromiss zwischen Auswendiglernen und Leistung zu kontrollieren\". <\/span>erkl\u00e4rte Giannis Daras<\/span><\/a>, ein Doktorand der Informatik, der die Arbeit leitete.\u00a0<\/span><\/p>\n

\"Mit zunehmender Korruption w\u00e4hrend des Trainings nimmt die Merkf\u00e4higkeit der Trainingsmenge ab\".<\/span><\/p>\n

Wissenschaftliche und medizinische Anwendungen<\/h2>\n

Die Einsatzm\u00f6glichkeiten von Ambient Diffusion gehen \u00fcber die L\u00f6sung von Urheberrechtsfragen hinaus.\u00a0<\/span><\/p>\n

Laut Professor Adam Klivans, einem Mitarbeiter des Projekts, \"k\u00f6nnte sich das System auch f\u00fcr wissenschaftliche und medizinische Anwendungen als n\u00fctzlich erweisen. Das gilt im Grunde f\u00fcr alle Forschungsarbeiten, bei denen es teuer oder unm\u00f6glich ist, \u00fcber einen vollst\u00e4ndigen Satz unverf\u00e4lschter Daten zu verf\u00fcgen, von der Abbildung schwarzer L\u00f6cher bis hin zu bestimmten Arten von MRT-Scans\".<\/span><\/p>\n

Dies ist besonders vorteilhaft in Bereichen mit begrenztem Zugang zu unverf\u00e4lschten Daten, wie z. B. Astronomie und Teilchenphysik<\/a>.\u00a0<\/span><\/p>\n

In diesen und anderen Bereichen k\u00f6nnen die Daten extrem verrauscht, von schlechter Qualit\u00e4t oder sp\u00e4rlich sein, was bedeutet, dass sinnvolle Daten durch nutzlose Daten stark in der Minderheit sind. Hier w\u00e4re es hilfreich, Modellen beizubringen, suboptimale Daten effizienter zu nutzen.<\/p>\n

Wenn der Ambient Diffusion-Ansatz weiter verfeinert w\u00fcrde, k\u00f6nnten KI-Unternehmen funktionale Text-Bild-Modelle erstellen und dabei die Rechte der Urheber von Originalinhalten respektieren und rechtliche Probleme vermeiden.<\/p>\n

Das w\u00fcrde zwar nicht die Bedenken ausr\u00e4umen, dass KI-Bildbearbeitungsprogramme das Arbeitsangebot f\u00fcr echte K\u00fcnstler einschr\u00e4nken, aber es w\u00fcrde zumindest deren Werke davor sch\u00fctzen, dass sie versehentlich in Ausgaben kopiert werden.<\/p>","protected":false},"excerpt":{"rendered":"

Forscher der University of Texas in Austin haben einen innovativen Rahmen f\u00fcr das Training von KI-Modellen auf stark besch\u00e4digten Bildern entwickelt. Diese als Ambient Diffusion bezeichnete Methode erm\u00f6glicht es KI-Modellen, sich von Bildern inspirieren zu lassen, ohne sie direkt zu kopieren. Bei herk\u00f6mmlichen Text-Bild-Modellen, wie sie von DALL-E, Midjourney und Stable Diffusion verwendet werden, besteht die Gefahr von Urheberrechtsverletzungen, da sie auf Datens\u00e4tzen trainiert werden, die urheberrechtlich gesch\u00fctzte Bilder enthalten, was dazu f\u00fchrt, dass sie diese Bilder manchmal versehentlich nachahmen. Ambient Diffusion stellt dies auf den Kopf, indem es Modelle mit absichtlich verf\u00e4lschten Daten trainiert. In der Studie hat das Forscherteam, zu dem auch Alex Dimakis und Giannis Daras von der Fakult\u00e4t f\u00fcr Elektrotechnik und Computertechnik geh\u00f6ren, eine neue Methode entwickelt.<\/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\/de\/2024\/05\/researchers-train-model-to-create-images-without-seeing-copyrighted-work\/\" \/>\n<meta property=\"og:locale\" content=\"de_DE\" \/>\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. Conventional text-to-image models used by DALL-E, Midjourney, and Stable Diffusion risk copyright infringement because they’re trained on datasets that include copyrighted images, leading them to sometimes inadvertently replicate those images.\u00a0 Ambient Diffusion flips that on its head by training models with deliberately corrupted data. 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\/de\/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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