Generative adversarial networks (GANs), a class of machine learning frameworks that can generate new texts, images, videos, and voice recordings, have been found to be highly valuable for tackling numerous real-world problems. .
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Generative adversarial networks (GANs), a class of machine learning frameworks that can generate new texts, images, videos, and voice recordings, have been found to be highly valuable for tackling numerous real-world problems. For instance, GANs have been successfully used to generate image datasets to train other deep learning algorithms, to generate videos or animations for specific uses, and to create suitable captions for images.
Researchers at the Computer Vision and Biometrics Lab of IIT Allahabad and Vignan University in India have recently developed a new GAN-based model that can generate compressed images from text-based descriptions. This model, introduced in a paper pre-published on arXiv, could open interesting possibilities for image storage and for the sharing of content between different smart devices.
„The idea of T2CI GAN is aligned with the theme of ‚direct processing/analytics of data in the compressed domain without full decompression,‘ on which we have been working on since 2012,“ Mohammed Javed, one of the researchers who carried out the study, told TechXplore. „However, the idea in T2CI GAN is a bit different, as here we wanted to produce/retrieve images in the compressed form given the text descriptions of the image.“
In their past studies, Javed and his colleagues used GANs and other deep learning models to tackle numerous tasks, including the extraction of features from data, the segmentation of text and image data, spotting words in large text excerpts, and to created compressed JPEG files.
