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- W2885444872 abstract "Text-to-Image translation has been an active area of research in the recent past. The ability for a network to learn the meaning of a sentence and generate an accurate image that depicts the sentence shows ability of the model to think more like humans. Popular methods on text to image translation make use of Generative Adversarial Networks (GANs) to generate high quality images based on text input, but the generated images don't always reflect the meaning of the sentence given to the model as input. We address this issue by using a captioning network to caption on generated images and exploit the distance between ground truth captions and generated captions to improve the network further. We show extensive comparisons between our method and existing methods." @default.
- W2885444872 created "2018-08-22" @default.
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- W2885444872 date "2018-08-14" @default.
- W2885444872 modified "2023-09-26" @default.
- W2885444872 title "Text-to-Image-to-Text Translation using Cycle Consistent Adversarial Networks" @default.
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- W2885444872 doi "https://doi.org/10.48550/arxiv.1808.04538" @default.
- W2885444872 hasPublicationYear "2018" @default.
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