{"id":2680,"date":"2023-07-18T20:48:46","date_gmt":"2023-07-18T20:48:46","guid":{"rendered":"https:\/\/dailyai.com\/?p=2680"},"modified":"2023-07-18T21:23:28","modified_gmt":"2023-07-18T21:23:28","slug":"ai-image-generators-quality-suffers-when-trained-on-ai-generated-data","status":"publish","type":"post","link":"https:\/\/dailyai.com\/nl\/2023\/07\/ai-image-generators-quality-suffers-when-trained-on-ai-generated-data\/","title":{"rendered":"De kwaliteit van AI-afbeeldingsgeneratoren lijdt eronder als ze worden getraind op AI-gegenereerde gegevens"},"content":{"rendered":"

Een nieuw onderzoek toont aan dat het trainen van AI-afbeeldingsgeneratoren met AI-gegenereerde afbeeldingen uiteindelijk leidt tot een significante vermindering van de uitvoerkwaliteit.\u00a0<\/b><\/p>\n

Baraniuk en zijn team lieten zien hoe deze problematische AI-trainingslus generatieve AI's be\u00efnvloedt, waaronder StyleGAN en diffusiemodellen. Deze behoren tot de modellen die worden gebruikt voor AI-beeldgeneratoren zoals Stable Diffusion, DALL-E en MidJourney.\u00a0<\/span><\/p>\n

In hun experiment<\/a>trainde het team de AI's op zowel door AI gegenereerde als echte afbeeldingen. 70.000 echte menselijke gezichten afkomstig van Flickr.<\/span><\/p>\n

Toen elke AI werd getraind op zijn eigen AI-gegenereerde beelden, begonnen de uitvoerresultaten van de StyleGAN beeldgenerator vervormde en golvende visuele patronen te vertonen, terwijl de uitvoerresultaten van de diffusiebeeldgenerator waziger werden.<\/span><\/p>\n

In beide gevallen resulteerde het trainen van AI's op door AI gegenereerde afbeeldingen in kwaliteitsverlies.\u00a0<\/span><\/p>\n

Een van de <\/span>onderzoek<\/span> De auteurs, Richard Baraniuk van de Rice University in Texas, waarschuwen: \"Er komt een glibberig pad naar het gebruik van synthetische gegevens, bewust of onbewust.\"<\/span><\/p>\n

\"AI
Hoe meer AI-modellen worden getraind op synthetische beelden, hoe onscherper hun resultaten worden. Bron: Universiteit van Rice<\/a>.<\/figcaption><\/figure>\n

Hoewel de afname in beeldkwaliteit werd verminderd door AI-gegenereerde beelden van hogere kwaliteit te selecteren voor training, leidde dit tot een verlies aan beelddiversiteit.\u00a0<\/span><\/p>\n

De onderzoekers probeerden ook een vaste set echte afbeeldingen op te nemen in trainingssets die voornamelijk AI-gegenereerde afbeeldingen bevatten, een methode die soms wordt gebruikt om kleine trainingssets aan te vullen.\u00a0<\/span><\/p>\n

Het lijkt onvermijdelijk dat hoe meer AI-gegenereerde gegevens in de trainingsdatasets terechtkomen, hoe slechter de output wordt. Het is gewoon een kwestie van wanneer.<\/span><\/p>\n

Er werden redelijke resultaten behaald toen elke AI werd getraind op een mix van door de AI gegenereerde afbeeldingen en een constant veranderende set authentieke afbeeldingen. Dit hielp om de kwaliteit en diversiteit van de afbeeldingen te behouden.<\/span><\/p>\n

Het is een uitdaging om een balans te vinden tussen kwantiteit en kwaliteit - synthetische beelden zijn potentieel onbeperkt in vergelijking met echte beelden, maar het gebruik ervan brengt kosten met zich mee.\u00a0<\/span><\/p>\n

AI's raken door hun gegevens heen<\/span><\/h2>\n

AI's zijn hongerig naar gegevens, maar authentieke gegevens van hoge kwaliteit zijn een eindige bron.\u00a0<\/span><\/p>\n

De bevindingen in dit onderzoek weerspiegelen <\/span>soortgelijke studies voor het genereren van tekst<\/span><\/a>waar AI-outputs de neiging hebben te lijden wanneer modellen worden getraind op AI-gegenereerde tekst.\u00a0<\/span><\/p>\n

De onderzoekers benadrukken dat kleinere organisaties met beperkte mogelijkheden om authentieke gegevens te verzamelen de grootste uitdagingen hebben om AI-gegenereerde beelden uit hun datasets te filteren.\u00a0<\/span><\/p>\n

Bovendien wordt het probleem nog verergerd doordat het internet wordt overspoeld met AI-gegenereerde content, waardoor het verbluffend lastig wordt om te bepalen op welk type gegevens modellen zijn getraind.\u00a0<\/span><\/p>\n

Sina Alemohammad, van Rice University, suggereert dat het ontwikkelen van watermerken om AI-gegenereerde afbeeldingen te identificeren zou kunnen helpen, maar waarschuwt dat verborgen watermerken die over het hoofd worden gezien de kwaliteit van AI-gegenereerde afbeeldingen kunnen aantasten.<\/span><\/p>\n

Alemohammad concludeert: \"Je bent verdoemd als je het doet en verdoemd als je het niet doet. Maar het is zeker beter om een watermerk op de afbeelding te zetten dan niet.\"<\/span><\/p>\n

Er wordt druk gedebatteerd over de langetermijngevolgen van het consumeren van de output van AI, maar op dit moment moeten AI-ontwikkelaars oplossingen vinden om de kwaliteit van hun modellen te waarborgen. <\/span><\/p>","protected":false},"excerpt":{"rendered":"

Een nieuwe studie toont aan dat het trainen van AI-afbeeldingsgeneratoren met AI-gegenereerde afbeeldingen uiteindelijk leidt tot een aanzienlijke vermindering van de uitvoerkwaliteit. Baraniuk en zijn team hebben aangetoond hoe deze problematische AI-trainingslus generatieve AI's be\u00efnvloedt, waaronder StyleGAN en diffusiemodellen. Deze behoren tot de modellen die worden gebruikt voor AI-beeldgeneratoren zoals Stable Diffusion, DALL-E en MidJourney. In hun experiment trainde het team de AI's op zowel door AI gegenereerde als echte afbeeldingen. 70.000 echte menselijke gezichten afkomstig van Flickr. Toen elke AI werd getraind op zijn eigen door AI gegenereerde afbeeldingen, begon de uitvoer van de StyleGAN-afbeeldingsgenerator vervormde en golvende visuele patronen te vertonen,<\/p>","protected":false},"author":2,"featured_media":2682,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[233,234,174],"class_list":["post-2680","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-image-generator","tag-midjourney","tag-stable-diffusion"],"yoast_head":"\nAI image generators' quality suffers when trained on AI-generated data | DailyAI<\/title>\n<meta name=\"description\" content=\"A new study shows that training AI image generators with AI-generated images could lead to a self-consuming loop.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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