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You're tasked with building a system that can generate realistic images from text descriptions and, conversely, generate accurate text descriptions from images. You decide to use a GAN (Generative Adversarial Network) architecture, but need to handle both modalities effectively. What GAN variant would be MOST suitable for this bi-directional multimodal task?
Correct Answer: C
CycleGAN is designed for unpaired image-to-image translation. In this scenario, it can be adapted to translate between the image and text modalities without requiring paired data. One generator learns to generate images from text, while another learns to generate text from images. Cycle consistency ensures that translating an image to text and then back to an image results in an image similar to the original. Vanilla GANI cGAN, and DCGAN are not inherently designed for bi- directional translation between modalities without paired data. SRGAN is for image super-resolution.