Публікація:
A survey of methods of text-to-image translation

dc.contributor.authorKonarieva, I.
dc.contributor.authorPydorenko, D.
dc.contributor.authorTuruta, O.
dc.date.accessioned2021-11-28T15:26:17Z
dc.date.available2021-11-28T15:26:17Z
dc.date.issued2019
dc.description.abstractThe given work considers the existing methods of text compression (finding keywords or creating summary) using RAKE, Lex Rank, Luhn, LSA, Text Rank algorithms; image generation; text-to-image and image-to-image translation including GANs (generative adversarial networks). Different types of GANs were described such as StyleGAN, GauGAN, Pix2Pix, CycleGAN, BigGAN, AttnGAN. This work aims to show ways to create illustrations for the text. First, key information should be obtained from the text. Second, this key information should be transformed into images. There were proposed several ways to transform keywords to images: generating images or selecting them from a dataset with further transforming like generating new images based on selected ow combining selected images e.g. with applying style from one image to another. Based on results, possibilities for further improving the quality of image generation were also planned: combining image generation with selecting images from a dataset, limiting topics of image generation.uk_UA
dc.identifier.citationKonarieva I. A survey of methods of text-to-image translation / Konarieva I., Pydorenko D., Turuta O. // Бионика интеллекта : научно-технический журнал. – 2019. – № 2 (93). – С. 64–68.uk_UA
dc.identifier.urihttps://openarchive.nure.ua/handle/document/18430
dc.language.isoenuk_UA
dc.publisherХНУРЕuk_UA
dc.subjectImage generationuk_UA
dc.subjectText keywordsuk_UA
dc.subjectImage-to-image translationuk_UA
dc.subjectText-to-image translationuk_UA
dc.subjectText compressionuk_UA
dc.titleA survey of methods of text-to-image translationuk_UA
dc.typeArticleuk_UA
dspace.entity.typePublication

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