{"id":11850,"date":"2024-04-30T12:48:52","date_gmt":"2024-04-30T12:48:52","guid":{"rendered":"https:\/\/dailyai.com\/?p=11850"},"modified":"2024-04-30T15:58:32","modified_gmt":"2024-04-30T15:58:32","slug":"googles-gecko-benchmark-identifies-best-ai-image-generator","status":"publish","type":"post","link":"https:\/\/dailyai.com\/da\/2024\/04\/googles-gecko-benchmark-identifies-best-ai-image-generator\/","title":{"rendered":"Googles Gecko-benchmark identificerer den bedste AI-billedgenerator"},"content":{"rendered":"

Googles DeepMind<\/span><\/span> udgav Gecko, et nyt benchmark til omfattende evaluering af AI-tekst-til-billede-modeller (T2I).<\/strong><\/p>\n

I l\u00f8bet af de sidste to \u00e5r har vi set AI-billedgeneratorer som DALL-E<\/span><\/span><\/a> og Midt p\u00e5 rejsen<\/a> bliver gradvist bedre for hver version, der udgives.<\/p>\n

Men beslutningen om, hvilken af de underliggende modeller, disse platforme bruger, der er bedst, har i h\u00f8j grad v\u00e6ret subjektiv og vanskelig at benchmarke.<\/p>\n

Det er ikke s\u00e5 enkelt at p\u00e5st\u00e5, at en model er \"bedre\" end en anden. Forskellige modeller udm\u00e6rker sig i forskellige aspekter af billedgenereringen. En er m\u00e5ske god til tekstgengivelse, mens en anden er bedre til objektinteraktion.<\/p>\n

En vigtig udfordring for T2I-modeller er at f\u00f8lge hver eneste detalje i prompten og f\u00e5 dem afspejlet n\u00f8jagtigt i det genererede billede.<\/p>\n

Med Gecko er DeepMind<\/span><\/span> Forskere har skabt en Benchmark<\/a> der evaluerer T2I-modellernes evner p\u00e5 samme m\u00e5de, som mennesker g\u00f8r det.<\/p>\n

F\u00e6rdighedss\u00e6t<\/h2>\n

Forskerne definerede f\u00f8rst et omfattende datas\u00e6t med f\u00e6rdigheder, der er relevante for T2I-generering. Disse omfatter bl.a. rumlig forst\u00e5else, handlingsgenkendelse og tekstgengivelse. De opdelte dem yderligere i mere specifikke underf\u00e6rdigheder.<\/p>\n

Under tekstgengivelse kan delf\u00e6rdighederne f.eks. omfatte gengivelse af forskellige skrifttyper, farver eller tekstst\u00f8rrelser.<\/p>\n

En LLM blev derefter brugt til at generere prompts til at teste T2I-modellens evne til en specifik f\u00e6rdighed eller delf\u00e6rdighed.<\/p>\n

Det g\u00f8r det muligt for skaberne af en T2I-model at finde ud af, ikke bare hvilke f\u00e6rdigheder der er udfordrende, men ogs\u00e5 p\u00e5 hvilket kompleksitetsniveau en f\u00e6rdighed bliver udfordrende for deres model.<\/p>\n

\"\"
Gecko-benchmarkrammen bruger et datas\u00e6t med f\u00e6rdigheder og underf\u00e6rdigheder (a), menneskelig Likert-scoring af billedn\u00f8jagtighed (b), LLM-genererede foresp\u00f8rgsler til VQA-analyse og resulterer i omfattende m\u00e5linger, der korrelerer med menneskelige evalueringer. Kilde: arXiv<\/figcaption><\/figure>\n

Menneskelig vs. automatisk evaluering<\/h2>\n

Gecko m\u00e5ler ogs\u00e5, hvor pr\u00e6cist en T2I-model f\u00f8lger alle detaljerne i en prompt. Igen blev en LLM brugt til at isolere vigtige detaljer i hver input-prompt og derefter generere et s\u00e6t sp\u00f8rgsm\u00e5l relateret til disse detaljer.<\/p>\n

Disse sp\u00f8rgsm\u00e5l kan b\u00e5de v\u00e6re enkle, direkte sp\u00f8rgsm\u00e5l om synlige elementer i billedet (f.eks. \"Er der en kat p\u00e5 billedet?\") og mere komplekse sp\u00f8rgsm\u00e5l, der tester forst\u00e5elsen af scenen eller forholdet mellem objekter (f.eks. \"Sidder katten over bogen?\").<\/p>\n

En VQA-model (Visual Question Answering) analyserer derefter det genererede billede og besvarer sp\u00f8rgsm\u00e5lene for at se, hvor pr\u00e6cist T2I-modellen tilpasser sit output-billede til en input-prompt.<\/p>\n

Forskerne indsamlede over 100.000 menneskelige kommentarer, hvor deltagerne vurderede et genereret billede ud fra, hvor godt billedet passede til specifikke kriterier.<\/p>\n

Menneskene blev bedt om at overveje et specifikt aspekt af input-prompten og score billedet p\u00e5 en skala fra 1 til 5 baseret p\u00e5, hvor godt det passede til prompten.<\/p>\n

Ved at bruge de menneskeligt kommenterede evalueringer som guldstandard kunne forskerne bekr\u00e6fte, at deres auto-eval-metrik \"er bedre korreleret med menneskelige vurderinger end eksisterende metrikker for vores nye datas\u00e6t.\"<\/p>\n

Resultatet er et benchmarking-system, der er i stand til at s\u00e6tte tal p\u00e5 specifikke faktorer, der g\u00f8r et genereret billede godt eller d\u00e5rligt.<\/p>\n

Gecko scorer i bund og grund outputbilledet p\u00e5 en m\u00e5de, der ligger t\u00e6t op ad, hvordan vi intuitivt beslutter, om vi er tilfredse med det genererede billede eller ej.<\/p>\n

S\u00e5 hvad er den bedste tekst-til-billede-model?<\/p>\n

I deres papir<\/a>Forskerne konkluderede, at Googles Muse-model sl\u00e5r Stable Diffusion 1.5 og SDXL p\u00e5 Gecko-benchmarket. De er m\u00e5ske forudindtagede, men tallene lyver ikke.<\/p>","protected":false},"excerpt":{"rendered":"

Googles DeepMind har udgivet Gecko, et nyt benchmark til omfattende evaluering af AI-tekst-til-billede-modeller (T2I). I l\u00f8bet af de sidste to \u00e5r har vi set AI-billedgeneratorer som DALL-E og Midjourney blive gradvist bedre med hver versionsudgivelse. Men beslutningen om, hvilken af de underliggende modeller, som disse platforme bruger, der er bedst, har i h\u00f8j grad v\u00e6ret subjektiv og vanskelig at benchmarke. Det er ikke s\u00e5 enkelt at h\u00e6vde, at en model er \"bedre\" end en anden. Forskellige modeller udm\u00e6rker sig i forskellige aspekter af billedgenereringen. En kan v\u00e6re god til tekstgengivelse, mens en anden kan v\u00e6re bedre til objektinteraktion. En vigtig udfordring for T2I-modeller<\/p>","protected":false},"author":6,"featured_media":11853,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[84],"tags":[147,107,102,165],"class_list":["post-11850","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry","tag-deepmind","tag-generative-ai","tag-google","tag-image-generation"],"yoast_head":"\nGoogle\u2019s Gecko benchmark identifies best AI image generator | 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\/da\/2024\/04\/googles-gecko-benchmark-identifies-best-ai-image-generator\/\" \/>\n<meta property=\"og:locale\" content=\"da_DK\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Google\u2019s Gecko benchmark identifies best AI image generator | DailyAI\" \/>\n<meta property=\"og:description\" content=\"Google\u2019s DeepMind released Gecko, a new benchmark for comprehensively evaluating AI text-to-image (T2I) models. 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