{"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\/pt\/2023\/07\/ai-image-generators-quality-suffers-when-trained-on-ai-generated-data\/","title":{"rendered":"A qualidade dos geradores de imagens de IA \u00e9 afetada quando treinados com dados gerados por IA"},"content":{"rendered":"

Um novo estudo mostra que o treino de geradores de imagens de IA com imagens geradas por IA acaba por conduzir a uma redu\u00e7\u00e3o significativa da qualidade dos resultados.\u00a0<\/b><\/p>\n

Baraniuk e a sua equipa demonstraram como este problem\u00e1tico ciclo de forma\u00e7\u00e3o de IA afecta as IAs generativas, incluindo StyleGAN e modelos de difus\u00e3o. Estes est\u00e3o entre os modelos utilizados para geradores de imagens de IA como Stable Diffusion, DALL-E e MidJourney.\u00a0<\/span><\/p>\n

Na sua experi\u00eancia<\/a>A equipa treinou as IAs com imagens reais ou geradas por IA. 70.000 rostos humanos reais provenientes do Flickr.<\/span><\/p>\n

Quando cada IA foi treinada com as suas pr\u00f3prias imagens geradas pela IA, os resultados do gerador de imagens StyleGAN come\u00e7aram a apresentar padr\u00f5es visuais distorcidos e ondulados, enquanto os resultados do gerador de imagens de difus\u00e3o se tornaram mais desfocados.<\/span><\/p>\n

Em ambos os casos, o treino de IAs em imagens geradas por IA resultou numa perda de qualidade.\u00a0<\/span><\/p>\n

Um dos <\/span>estudo<\/span> Os autores, Richard Baraniuk, da Universidade de Rice, no Texas, advertem: \"Vai haver um declive escorregadio para a utiliza\u00e7\u00e3o de dados sint\u00e9ticos, quer de forma intencional quer involunt\u00e1ria\".<\/span><\/p>\n

\"Gerador
Quanto mais os modelos de IA s\u00e3o treinados em imagens sint\u00e9ticas, mais desfocados ficam os seus resultados. Fonte: Universidade de Rice<\/a>.<\/figcaption><\/figure>\n

Embora o decl\u00ednio na qualidade da imagem tenha sido reduzido atrav\u00e9s da sele\u00e7\u00e3o de imagens de maior qualidade geradas por IA para treino, isto levou a uma perda de diversidade de imagens.\u00a0<\/span><\/p>\n

Os investigadores tamb\u00e9m tentaram incorporar um conjunto fixo de imagens reais em conjuntos de treino que inclu\u00edam principalmente imagens geradas por IA, um m\u00e9todo por vezes utilizado para complementar pequenos conjuntos de treino.\u00a0<\/span><\/p>\n

No entanto, isto apenas atrasou o decl\u00ednio da qualidade da imagem - parece inevit\u00e1vel que quanto mais dados gerados pela IA entrarem nos conjuntos de dados de treino, pior ser\u00e1 o resultado. \u00c9 apenas uma quest\u00e3o de saber quando.<\/span><\/p>\n

Obtiveram-se resultados razo\u00e1veis quando cada IA foi treinada com uma mistura de imagens geradas pela IA e um conjunto de imagens aut\u00eanticas em constante mudan\u00e7a. Isto ajudou a manter a qualidade e a diversidade das imagens.<\/span><\/p>\n

\u00c9 um desafio equilibrar a quantidade com a qualidade - as imagens sint\u00e9ticas s\u00e3o potencialmente ilimitadas em compara\u00e7\u00e3o com as imagens reais, mas a sua utiliza\u00e7\u00e3o tem um custo.\u00a0<\/span><\/p>\n

As IA est\u00e3o a ficar sem dados<\/span><\/h2>\n

As IA s\u00e3o \u00e1vidas de dados, mas os dados aut\u00eanticos e de alta qualidade s\u00e3o um recurso finito.\u00a0<\/span><\/p>\n

Os resultados desta investiga\u00e7\u00e3o reflectem <\/span>estudos semelhantes para a gera\u00e7\u00e3o de texto<\/span><\/a>em que os resultados da IA tendem a sofrer quando os modelos s\u00e3o treinados em texto gerado pela IA.\u00a0<\/span><\/p>\n

Os investigadores salientam que as organiza\u00e7\u00f5es mais pequenas, com capacidade limitada para recolher dados aut\u00eanticos, enfrentam os maiores desafios na filtragem de imagens geradas por IA dos seus conjuntos de dados.\u00a0<\/span><\/p>\n

Al\u00e9m disso, a quest\u00e3o \u00e9 agravada pelo facto de a Internet estar a ficar inundada de conte\u00fados gerados por IA, o que torna extremamente dif\u00edcil determinar o tipo de dados em que os modelos s\u00e3o treinados.\u00a0<\/span><\/p>\n

Sina Alemohammad, da Universidade de Rice, sugere que o desenvolvimento de marcas de \u00e1gua para identificar imagens geradas por IA pode ser \u00fatil, mas adverte que marcas de \u00e1gua ocultas podem degradar a qualidade das imagens geradas por IA.<\/span><\/p>\n

Alemohammad conclui: \"\u00c9-se condenado se o fizer e condenado se n\u00e3o o fizer. Mas \u00e9 definitivamente melhor colocar uma marca de \u00e1gua na imagem do que n\u00e3o a colocar.\"<\/span><\/p>\n

As consequ\u00eancias a longo prazo da IA que consome os seus resultados s\u00e3o muito debatidas, mas, neste momento, os criadores de IA precisam de encontrar solu\u00e7\u00f5es para garantir a qualidade dos seus modelos. <\/span><\/p>","protected":false},"excerpt":{"rendered":"

Um novo estudo mostra que o treino de geradores de imagens de IA com imagens geradas por IA acaba por conduzir a uma redu\u00e7\u00e3o significativa da qualidade dos resultados. Baraniuk e a sua equipa demonstraram como este problem\u00e1tico ciclo de treino de IA afecta as IAs generativas, incluindo StyleGAN e modelos de difus\u00e3o. Estes est\u00e3o entre os modelos utilizados para geradores de imagens de IA como Stable Diffusion, DALL-E e MidJourney. Na sua experi\u00eancia, a equipa treinou as IAs com imagens reais ou geradas por IA. 70.000 rostos humanos reais provenientes do Flickr. Quando cada IA foi treinada com as suas pr\u00f3prias imagens geradas por IA, os resultados do gerador de imagens StyleGAN come\u00e7aram a apresentar padr\u00f5es visuais distorcidos e ondulados,<\/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 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