{"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\/nb\/2023\/07\/ai-image-generators-quality-suffers-when-trained-on-ai-generated-data\/","title":{"rendered":"Kvaliteten p\u00e5 AI-bildegeneratorer blir d\u00e5rligere n\u00e5r de trenes opp p\u00e5 AI-genererte data"},"content":{"rendered":"

En ny studie viser at trening av AI-bildegeneratorer med AI-genererte bilder til slutt f\u00f8rer til en betydelig reduksjon i kvaliteten p\u00e5 utdataene.\u00a0<\/b><\/p>\n

Baraniuk og teamet hans demonstrerte hvordan denne problematiske AI-treningssl\u00f8yfen p\u00e5virker generativ AI, inkludert StyleGAN og diffusjonsmodeller. Disse er blant modellene som brukes i AI-bildegeneratorer som Stable Diffusion, DALL-E og MidJourney.\u00a0<\/span><\/p>\n

I deres eksperiment<\/a>teamet trente AI-ene p\u00e5 enten AI-genererte eller ekte bilder. 70 000 ekte menneskeansikter hentet fra Flickr.<\/span><\/p>\n

N\u00e5r hver AI ble trent p\u00e5 sine egne AI-genererte bilder, begynte StyleGAN-bildegeneratorens utdata \u00e5 vise forvrengte og b\u00f8lgete visuelle m\u00f8nstre, mens diffusjonsbildegeneratorens utdata ble mer uskarpe.<\/span><\/p>\n

I begge tilfeller f\u00f8rte oppl\u00e6ring av AI p\u00e5 AI-genererte bilder til et tap av kvalitet.\u00a0<\/span><\/p>\n

En av de <\/span>studie<\/span> Richard Baraniuk fra Rice University i Texas, advarer: \"Det kommer til \u00e5 bli et skr\u00e5plan \u00e5 bruke syntetiske data, enten bevisst eller ubevisst.\"<\/span><\/p>\n

\"AI-bildegenerator\"
Jo mer AI-modeller trenes p\u00e5 syntetiske bilder, desto mer uskarpe blir resultatene. Kilde: Rice University<\/a>.<\/figcaption><\/figure>\n

Selv om nedgangen i bildekvalitet ble redusert ved \u00e5 velge AI-genererte bilder av h\u00f8yere kvalitet til oppl\u00e6ring, f\u00f8rte dette til et tap av bildediversitet.\u00a0<\/span><\/p>\n

Forskerne pr\u00f8vde ogs\u00e5 \u00e5 inkorporere et fast sett med ekte bilder i treningssett som prim\u00e6rt inneholdt AI-genererte bilder, en metode som noen ganger brukes for \u00e5 supplere sm\u00e5 treningssett.\u00a0<\/span><\/p>\n

Dette forsinket imidlertid bare nedgangen i bildekvaliteten - det virker uunng\u00e5elig at jo mer AI-genererte data som kommer inn i oppl\u00e6ringsdatasettene, desto d\u00e5rligere blir resultatet. Det er bare et sp\u00f8rsm\u00e5l om n\u00e5r.<\/span><\/p>\n

Vi oppn\u00e5dde rimelige resultater da hver AI ble trent opp p\u00e5 en blanding av AI-genererte bilder og et sett med autentiske bilder som stadig ble endret. Dette bidro til \u00e5 opprettholde kvaliteten og mangfoldet i bildene.<\/span><\/p>\n

Det er utfordrende \u00e5 balansere kvantitet og kvalitet - syntetiske bilder er potensielt ubegrensede sammenlignet med ekte bilder, men det koster \u00e5 bruke dem.\u00a0<\/span><\/p>\n

Den kunstige intelligensen g\u00e5r tom for data<\/span><\/h2>\n

Kunstig intelligens er datahungrige, men autentiske data av h\u00f8y kvalitet er en begrenset ressurs.\u00a0<\/span><\/p>\n

Funnene i denne unders\u00f8kelsen gjenspeiler <\/span>lignende studier for tekstgenerering<\/span><\/a>, der AI-resultater har en tendens til \u00e5 lide n\u00e5r modeller trenes p\u00e5 AI-genererte tekster.\u00a0<\/span><\/p>\n

Forskerne fremhever at mindre organisasjoner med begrenset mulighet til \u00e5 samle inn autentiske data st\u00e5r overfor de st\u00f8rste utfordringene n\u00e5r det gjelder \u00e5 filtrere AI-genererte bilder fra datasettene sine.\u00a0<\/span><\/p>\n

I tillegg forsterkes problemet av at internett oversv\u00f8mmes av AI-generert innhold, noe som gj\u00f8r det utrolig vanskelig \u00e5 avgj\u00f8re hvilken type data modellene er oppl\u00e6rt p\u00e5.\u00a0<\/span><\/p>\n

Sina Alemohammad fra Rice University mener at det kan v\u00e6re nyttig \u00e5 utvikle vannmerker for \u00e5 identifisere AI-genererte bilder, men advarer samtidig om at skjulte vannmerker kan forringe kvaliteten p\u00e5 AI-genererte bilder.<\/span><\/p>\n

Alemohammad konkluderer: \"Du er forbannet hvis du gj\u00f8r det, og forbannet hvis du ikke gj\u00f8r det. Men det er definitivt bedre \u00e5 vannmerke bildet enn \u00e5 la v\u00e6re.\"<\/span><\/p>\n

De langsiktige konsekvensene av at AI bruker det den produserer, er omdiskutert, men akkurat n\u00e5 m\u00e5 AI-utviklere finne l\u00f8sninger for \u00e5 sikre kvaliteten p\u00e5 modellene sine. <\/span><\/p>","protected":false},"excerpt":{"rendered":"

En ny studie viser at trening av AI-bildegeneratorer med AI-genererte bilder til slutt f\u00f8rer til en betydelig reduksjon i utgangskvaliteten. Baraniuk og teamet hans demonstrerte hvordan denne problematiske AI-treningssl\u00f8yfen p\u00e5virker generative AI-er, inkludert StyleGAN- og diffusjonsmodeller. Disse er blant modellene som brukes til AI-bildegeneratorer som Stable Diffusion, DALL-E og MidJourney. I eksperimentet trente teamet AI-ene p\u00e5 enten AI-genererte eller ekte bilder. 70 000 ekte menneskeansikter hentet fra Flickr. N\u00e5r hver AI ble trent p\u00e5 sine egne AI-genererte bilder, begynte StyleGAN-bildegeneratorens utdata \u00e5 vise forvrengte og b\u00f8lgete visuelle m\u00f8nstre,<\/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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