T2CI GAN: A deep learning model that generates compressed images from text

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.